Abstract:The geometric state of the catenary is a crucial factor in ensuring the safety of traction power supply and the quality of current collection in high-speed railway systems. Understanding the fundamental characteristics of static and dynamic geometric parameters within the catenary system is vital for accurately interpreting them in both static and dynamic detection contexts. However, dynamic detection poses challenges due to factors such as vehicle posture changes, elastic deformation of the track surface, dynamic lifting of the contact wire, and environmental disturbances. Research efforts are concentrated on understanding the mechanisms behind hybrid error formation and developing compensation methods for detecting catenary geometry under dynamic conditions. This involves breaking down, tracking, and identifying hybrid errors within the complex interactions between the track, vehicle, and catenary, as well as environmental influences. The study examines the characteristics of vehicle posture errors and their representation models, taking into account the elastic deformation of the track surface, the dynamic lifting of the contact wire, and wind-induced responses. It further elaborates on compensation techniques for dynamic geometric errors in the catenary system, considering the combined effects of wheel-rail interaction, pantograph dynamics, and wind field forces. The goal is to uncover the processes of error generation, propagation, compound formation, and evolution during dynamic geometric detection of the catenary system under dynamic conditions. This research provides a theoretical foundation for controlling composite errors in dynamic geometric detection of high-speed catenary systems, aiming to scientifically and effectively improve China′s catenary system detection capabilities and ensure the high-quality maintenance of the system K .

Abstract:The geometric state of the catenary is a crucial factor in ensuring the safety of traction power supply and the quality of current collection in high-speed railway systems. Understanding the fundamental characteristics of static and dynamic geometric parameters within the catenary system is vital for accurately interpreting them in both static and dynamic detection contexts. However, dynamic detection poses challenges due to factors such as vehicle posture changes, elastic deformation of the track surface, dynamic lifting of the contact wire, and environmental disturbances. Research efforts are concentrated on understanding the mechanisms behind hybrid error formation and developing compensation methods for detecting catenary geometry under dynamic conditions. This involves breaking down, tracking, and identifying hybrid errors within the complex interactions between the track, vehicle, and catenary, as well as environmental influences. The study examines the characteristics of vehicle posture errors and their representation models, taking into account the elastic deformation of the track surface, the dynamic lifting of the contact wire, and wind-induced responses. It further elaborates on compensation techniques for dynamic geometric errors in the catenary system, considering the combined effects of wheel-rail interaction, pantograph dynamics, and wind field forces. The goal is to uncover the processes of error generation, propagation, compound formation, and evolution during dynamic geometric detection of the catenary system under dynamic conditions. This research provides a theoretical foundation for controlling composite errors in dynamic geometric detection of high-speed catenary systems, aiming to scientifically and effectively improve China′s catenary system detection capabilities and ensure the high-quality maintenance of the system.

Abstract:The normal operation of flight control sensors is the key of the safety and stability of the aircraft. However, most of the anomaly detection methods only consider the correlation between sensors data or the correlation change of data between different sensors. When the operating conditions of aircraft change dynamically, the accuracy of anomaly detection results may be low and the false alarm rate is high due to insufficient feature extraction. In this article, a flight control sensor data anomaly detection method based on spatio-temporal correlation is proposed to achieve the fusion modeling of sensor data changes in time and space. Firstly, the feature extraction module of temporal evolution and spatial correlation is formulated to extract the features in time and space in parallel. Secondly, the spatiotemporal correlation fusion is carried out to obtain spatio-temporal correlation predictive data. Finally, based on the statistic of the residual between the predicted data and the actual data, the threshold is selected and the sensor data is detected. Through the verification of simulation and measurement data, compared with typical anomaly detection methods such as RVM, the anomaly detection accuracy rate of the proposed method is at least 0.4% higher and the false alarm rate is at least 1.8% lower.

Abstract:The normal operation of flight control sensors is the key of the safety and stability of the aircraft. However, most of the anomaly detection methods only consider the correlation between sensors data or the correlation change of data between different sensors. When the operating conditions of aircraft change dynamically, the accuracy of anomaly detection results may be low and the false alarm rate is high due to insufficient feature extraction. In this article, a flight control sensor data anomaly detection method based on spatiotemporal correlation is proposed to achieve the fusion modeling of sensor data changes in time and space. Firstly, the feature extraction module of temporal evolution and spatial correlation is formulated to extract the features in time and space in parallel. Secondly, the spatio-temporal correlation fusion is carried out to obtain spatio-temporal correlation predictive data. Finally, based on the statistic of the residual between the predicted data and the actual data, the threshold is selected and the sensor data is detected. Through the verification of simulation and measurement data, compared with typical anomaly detection methods such as RVM, the anomaly detection accuracy rate of the proposed method is at least 0. 4% higher and the false alarm rate is at least 1. 8% lower.

Abstract:Transfer learning, as an effective technique to address distributional differences between domains, has received increasing interest in the field of fault diagnosis in recent years. However, the existing rotating machinery fault diagnosis methods usually fail to adequately consider the impact of different samples on the diagnostic results during the transfer learning process. In addition, the traditional edge distribution alignment method is not effective enough in reducing the distribution differences between source and target domains data, which largely limits the practical effectiveness of transfer learning methods. Aiming at the above problems, a rolling bearing fault diagnosis method based on dynamic calibration and joint distribution alignment is proposed. Firstly, the dynamically calibrated residual network (DCRN) is constructed as the feature extraction layer, which enhances the feature expression capability of the network by designing a dynamic calibration structure and adjusting the weights according to different samples. Secondly, the domain adaptive layer is designed and a new joint distribution alignment mechanism ( JDAM) is proposed. This mechanism gives full consideration to the edge distribution differences and condition distribution differences between the data of the source and target domains during feature alignment, enabling the effective transfer of knowledge learned in the source domain to the target domain and significantly improving the performance of the target task. Finally, the I-Softmax function is used to optimize the classifier, allowing the network to better identify the faults in different states. Experimental validation was given using the Case Western Reserve University bearing dataset, the MFS Bearing dataset and the roller gear dataset. Under cross-domain and variable noise conditions, the proposed method achieved average accuracies of 96. 50% , 96. 87% , and 94. 72% , respectively, demonstrating high fault diagnosis accuracy and good generalization capability.

Abstract:Transfer learning, as an effective technique to address distributional differences between domains, has received increasing interest in the field of fault diagnosis in recent years. However, the existing rotating machinery fault diagnosis methods usually fail to adequately consider the impact of different samples on the diagnostic results during the transfer learning process. In addition, the traditional edge distribution alignment method is not effective enough in reducing the distribution differences between source and target domains data, which largely limits the practical effectiveness of transfer learning methods. Aiming at the above problems, a rolling bearing fault diagnosis method based on dynamic calibration and joint distribution alignment is proposed. Firstly, the dynamically calibrated residual network (DCRN) is constructed as the feature extraction layer, which enhances the feature expression capability of the network by designing a dynamic calibration structure and adjusting the weights according to different samples. Secondly, the domain adaptive layer is designed and a new joint distribution alignment mechanism (JDAM) is proposed. This mechanism gives full consideration to the edge distribution differences and condition distribution differences between the data of the source and target domains during feature alignment, enabling the effective transfer of knowledge learned in the source domain to the target domain and significantly improving the performance of the target task. Finally, the I-Softmax function is used to optimize the classifier, allowing the network to better identify the faults in different states. Experimental validation was given using the Case Western Reserve University bearing dataset, the MFS Bearing dataset and the roller gear dataset. Under cross-domain and variable noise conditions, the proposed method achieved average accuracies of 96.50%, 96.87%, and 94.72%, respectively, demonstrating high fault diagnosis accuracy and good generalization capability.

Abstract:Working condition fluctuations, noise interference and other factors result in complex information in the high-frequency vibration signals of rolling bearings, making it difficult for the modeling of degradation processes to accurately reflect actual degradation trends, thus reducing the accuracy of remaining life predictions for the bearings. To address this issue, this paper proposes a long-range time-series correlation prediction method for bearing remaining life based on a signal decomposition network. The method uses a time series decomposition algorithm to break down vibration signals into trend, periodic, and residual components, effectively filtering out redundant information. For the rapid degradation to failure stages, a feature extraction model based on a long short-term memory network autoencoder is designed to derive health indicators with strong monotonicity and trend. Finally, a deep temporal autoregressive neural network model is developed to predict trends in these health indicators and to output the probability distribution of remaining life predictions. Experimental results show that the health indicators constructed in this study exhibit strong trends and monotonicity. Compared to other methods, the proposed remaining life prediction method achieves significantly higher accuracy.

Abstract:Working condition fluctuations, noise interference and other factors result in complex information in the high-frequency vibration signals of rolling bearings, making it difficult for the modeling of degradation processes to accurately reflect actual degradation trends, thus reducing the accuracy of remaining life predictions for the bearings. To address this issue, this paper proposes a long-range time-series correlation prediction method for bearing remaining life based on a signal decomposition network. The method uses a time series decomposition algorithm to break down vibration signals into trend, periodic, and residual components, effectively filtering out redundant information. For the rapid degradation to failure stages, a feature extraction model based on a long short-term memory network autoencoder is designed to derive health indicators with strong monotonicity and trend. Finally, a deep temporal autoregressive neural network model is developed to predict trends in these health indicators and to output the probability distribution of remaining life predictions. Experimental results show that the health indicators constructed in this study exhibit strong trends and monotonicity. Compared to other methods, the proposed remaining life prediction method achieves significantly higher accuracy.

Abstract:To assist experts in fault diagnosis, a sound signal coupling modulation model is proposed for compound faults caused by damage at different positions in a two-stage planetary gear system with a single-stage parallel gearbox. When compound faults occur in a wind turbine gearbox, their characteristic frequencies affect the meshing frequencies of different gear stages in the form of amplitude modulation and frequency modulation. Therefore, this paper proposes a coupling modulation model for the amplitude of sound signals in the wind turbine gearbox under compound faults. By utilizing a parameter identification approach, the modulation coefficients for different gear stages in the proposed coupling modulation model are determined. An energy ratio of sidebands index is constructed to evaluate the effectiveness of the identification. Finally, the reconstructed spectrum of the sound signal coupling modulation model is used to determine the location of compound faults, achieving auxiliary fault diagnosis. Experimental and field data analysis show that the sideband energy ratio indicators for evaluating the identification results are 0.948, 0.972, 0.977, and 0.964 3, effectively. These results effectively demonstrate the validity of the model identification, laying a foundation for automatic diagnosis of gearbox compound faults.

Abstract:To assist experts in fault diagnosis, a sound signal coupling modulation model is proposed for compound faults caused by damage at different positions in a two-stage planetary gear system with a single-stage parallel gearbox. When compound faults occur in a wind turbine gearbox, their characteristic frequencies affect the meshing frequencies of different gear stages in the form of amplitude modulation and frequency modulation. Therefore, this paper proposes a coupling modulation model for the amplitude of sound signals in the wind turbine gearbox under compound faults. By utilizing a parameter identification approach, the modulation coefficients for different gear stages in the proposed coupling modulation model are determined. An energy ratio of sidebands index is constructed to evaluate the effectiveness of the identification. Finally, the reconstructed spectrum of the sound signal coupling modulation model is used to determine the location of compound faults, achieving auxiliary fault diagnosis. Experimental and field data analysis show that the sideband energy ratio indicators for evaluating the identification results are 0. 948, 0. 972, 0. 977, and 0. 964 3, effectively. These results effectively demonstrate the validity of the model identification, laying a foundation for automatic diagnosis of gearbox compound faults.

Abstract:Efficient monitoring of leakage in urban water supply pipelines is of paramount importance for conserving water resources and ensuring the safety of residential water use. Existing leakage detection methods mainly rely on a single type of signal. Due to the inherent limitations of single signals, they are either insensitive to the minor fluctuations caused by small leaks or susceptible to interference from normal pipeline operations, which cannot be resolved through identification methods alone. This study exploits the respective advantages of acoustic and pressure signals and establishes a leakage monitoring strategy based on fusion of acoustic-pressure signals, effectively addressing the issue of high false positives in acoustic signals and high false negatives in pressure signals. The proposed strategy was validated through in-situ leak simulation tests on a long-distance operational water supply pipeline, evaluating the detection effectiveness and discussing the identifiable distance for both types of fluid parameter signals. The results demonstrate that the proposed strategy reduces the false alarm rate by 6. 02% and the miss detection rate by 4. 57% . Keywords:water supply pipeline; internal pipeline parameters monitoring; ada

Abstract:Efficient monitoring of leakage in urban water supply pipelines is of paramount importance for conserving water resources and ensuring the safety of residential water use. Existing leakage detection methods mainly rely on a single type of signal. Due to the inherent limitations of single signals, they are either insensitive to the minor fluctuations caused by small leaks or susceptible to interference from normal pipeline operations, which cannot be resolved through identification methods alone. This study exploits the respective advantages of acoustic and pressure signals and establishes a leakage monitoring strategy based on fusion of acousticpressure signals, effectively addressing the issue of high false positives in acoustic signals and high false negatives in pressure signals. The proposed strategy was validated through in-situ leak simulation tests on a long-distance operational water supply pipeline, evaluating the detection effectiveness and discussing the identifiable distance for both types of fluid parameter signals. The results demonstrate that the proposed strategy reduces the false alarm rate by 6.02% and the miss detection rate by 4.57%.

Abstract:The stress quantification in the stress concentration area of pipeline plays an important role in pipeline life evaluation and safety prevention. Weak magnetic detection technology is an effective stress concentration detection method. However, the presence of hard spots on the pipeline can generate similar signals to those of defects, interfering with stress quantification. This paper establishes an analytical model for the magnetic signal detection of non-volumetric damage in pipelines, analyzing the effects of hard spots and stress on the magnetic properties of the pipe material. The signal characteristics of hard point and stress concentration area are studied under different excitation intensities, and the dual magnetic field stress detection method using strong and weak excitation is proposed to eliminate the interference of hard point on stress weak magnetic signal. Experimental verification of the theoretical research was conducted. The results show that under weak excitation, the magnetization intensity decreases with the increase of stress and hardness. The attenuation gradient of magnetization increases with the increase of stress and decreases with the increase of hardness. Under strong magnetic excitation, the magnetization decreases linearly with the increase of hardness and is not affected by the change of stress. The signal characteristic magnetic sensitivity coefficient is introduced to characterize the detection ability of non-volume defects under different excitation intensities. Under 10 kA/ m excitation, the tangential magnetic sensitivity coefficient of magnetic signal increases from 1. 54 to 25. 87 with the increase of stress, and from 7. 46 to 33. 87 with the increase of hardness. Both stress and hard points have good recognition ability. Under a 30 kA/ m excitation, the tangential magnetic sensitivity coefficient of the magnetic signal increases from 0. 07 to 0. 54 as stress increases and from 0. 49 to 4 as hardness increases, with hard spots being well identified but low stress identification capability. Therefore, the use of strong and weak magnetic two-field detection method can eliminate the interference of hard point on stress detection.

Abstract:The stress quantification in the stress concentration area of pipeline plays an important role in pipeline life evaluation and safety prevention. Weak magnetic detection technology is an effective stress concentration detection method. However, the presence of hard spots on the pipeline can generate similar signals to those of defects, interfering with stress quantification. This paper establishes an analytical model for the magnetic signal detection of non-volumetric damage in pipelines, analyzing the effects of hard spots and stress on the magnetic properties of the pipe material. The signal characteristics of hard point and stress concentration area are studied under different excitation intensities, and the dual magnetic field stress detection method using strong and weak excitation is proposed to eliminate the interference of hard point on stress weak magnetic signal. Experimental verification of the theoretical research was conducted. The results show that under weak excitation, the magnetization intensity decreases with the increase of stress and hardness. The attenuation gradient of magnetization increases with the increase of stress and decreases with the increase of hardness. Under strong magnetic excitation, the magnetization decreases linearly with the increase of hardness and is not affected by the change of stress. The signal characteristic magnetic sensitivity coefficient is introduced to characterize the detection ability of non-volume defects under different excitation intensities. Under 10 kA/m excitation, the tangential magnetic sensitivity coefficient of magnetic signal increases from 1.54 to 25.87 with the increase of stress, and from 7.46 to 33.87 with the increase of hardness. Both stress and hard points have good recognition ability. Under a 30 kA/m excitation, the tangential magnetic sensitivity coefficient of the magnetic signal increases from 0.07 to 0.54 as stress increases and from 0.49 to 4 as hardness increases, with hard spots being well identified but low stress identification capability. Therefore, the use of strong and weak magnetic two-field detection method can eliminate the interference of hard point on stress detection.

Abstract:Addressing the challenge of accurately locating oblique cracks due to misjudgments of defect positions in single-probe electromagnetic ultrasound omni-directional guided wave detection, a two-probe circular fitting crack localization method based on tangent approximation is proposed. This method uses the electromagnetic acoustic transducer (EMAT) detection position point as the center of a circle and the acoustic range of the crack echo signals as the radius. By performing circular fitting at multiple different locations, a set of circle collections is obtained, allowing for the discrimination between true and false defect locations. The double-probe data compensation method is employed to obtain two sets of circles at different locations. By identifying the common tangent line of these two sets of circles, pseudo-tangents are removed, enabling accurate crack positioning and detection. Results show that the dual-probe circular fitting crack localization method effectively eliminates pseudo-tangents compared to the single probe, achieving a maximum crack localization error of 2.3%. This provides a reliable basis for accurate oblique crack detection.

Abstract:Addressing the challenge of accurately locating oblique cracks due to misjudgments of defect positions in single-probe electromagnetic ultrasound omni-directional guided wave detection, a two-probe circular fitting crack localization method based on tangent approximation is proposed. This method uses the electromagnetic acoustic transducer (EMAT) detection position point as the center of a circle and the acoustic range of the crack echo signals as the radius. By performing circular fitting at multiple different locations, a set of circle collections is obtained, allowing for the discrimination between true and false defect locations. The double-probe data compensation method is employed to obtain two sets of circles at different locations. By identifying the common tangent line of these two sets of circles, pseudo-tangents are removed, enabling accurate crack positioning and detection. Results show that the dual-probe circular fitting crack localization method effectively eliminates pseudo-tangents compared to the single probe, achieving a maximum crack localization error of 2. 3% . This provides a reliable basis for accurate oblique crack detection.

Abstract:This article proposes a method of three-dimensional orientation of underwater leaking bubbles using a three-dimensional fiveelement hydrophone array. First, the orientation errors of the four-element planar array, five-element planar array, and the five-element stereo array are analyzed theoretically and simulated, and the relationships among the orientation errors, time delay, sound speed, and source position are compared. The results show that the five-element stereo hydrophone array is not affected by the sound speed error, has a smaller influence from the time delay error and source position, and has the best orientation effect. A hydrophone array with a radius of 20 cm is set up in a tank for experimentation. The four-element planar array and the five-element stereo array are used to collect sound signals from four positions of bubbles, and the first peak of the sound signal is used as the reference point for the first arrival time to calculate the delay. Ten direction estimates are made for each position. The experimental results show that the azimuth angle estimation accuracy of the two arrays is similar, the elevation angle estimation effect of the five-element stereo array is better than that of the four-element planar array, and the average orientation error of azimuth angle and elevation angle is less than 4°, which is consistent with the theoretical analysis. Therefore, the five-element stereo hydrophone array can estimate the azimuth angle and elevation angle of the leakage point at different positions, and can realize leakage point orientation based on bubble sound signals in the underwater environment.

Abstract:This article proposes a method of three-dimensional orientation of underwater leaking bubbles using a three-dimensional five-element hydrophone array. First, the orientation errors of the four-element planar array, five-element planar array, and the five-element stereo array are analyzed theoretically and simulated, and the relationships among the orientation errors, time delay, sound speed, and source position are compared. The results show that the five-element stereo hydrophone array is not affected by the sound speed error, has a smaller influence from the time delay error and source position, and has the best orientation effect. A hydrophone array with a radius of 20 cm is set up in a tank for experimentation. The four-element planar array and the five-element stereo array are used to collect sound signals from four positions of bubbles, and the first peak of the sound signal is used as the reference point for the first arrival time to calculate the delay. Ten direction estimates are made for each position. The experimental results show that the azimuth angle estimation accuracy of the two arrays is similar, the elevation angle estimation effect of the five-element stereo array is better than that of the four-element planar array, and the average orientation error of azimuth angle and elevation angle is less than 4°, which is consistent with the theoretical analysis. Therefore, the five-element stereo hydrophone array can estimate the azimuth angle and elevation angle of the leakage point at different positions, and can realize leakage point orientation based on bubble sound signals in the underwater environment.

Abstract:To address the distortion issues of the thin plate deformation reconstruction, this study developed a quadrilateral element model based on the area coordinate shape function. Unlike the traditional isoparametric transformation method reliant on the Jacobian matrix, this approach introduces area coordinates as an intermediary between isoparametric and Cartesian coordinates. This ensures linear conversion and second-order completeness of shape functions, eliminating precision degradation caused by element distortion in isoparametric transformations and enhancing the model′s adaptability for complex structures. Considering the practical limitations of sensor placement, the study also presents a reconstruction method based on single-surface strain, grounded in the first-order shear deformation theory of classical Kirchhoff plates. By developing a multi-objective particle swarm optimization model, the optimal sensor layout for single-surface arrangement was determined. Simulation and experimental validation on an antenna structure model demonstrated that with a maximum deformation of 60 mm, the root mean square error (RMSE) was 0. 72 mm, and the percentage error (PD) was 2. 89% . This method achieved high-precision deformation reconstruction of antenna structures and shows promise for application in the design and manufacturing of deformable antenna structures

Abstract:To address the distortion issues of the thin plate deformation reconstruction, this study developed a quadrilateral element model based on the area coordinate shape function. Unlike the traditional isoparametric transformation method reliant on the Jacobian matrix, this approach introduces area coordinates as an intermediary between isoparametric and Cartesian coordinates. This ensures linear conversion and second-order completeness of shape functions, eliminating precision degradation caused by element distortion in isoparametric transformations and enhancing the model′s adaptability for complex structures. Considering the practical limitations of sensor placement, the study also presents a reconstruction method based on single-surface strain, grounded in the first-order shear deformation theory of classical Kirchhoff plates. By developing a multi-objective particle swarm optimization model, the optimal sensor layout for single-surface arrangement was determined. Simulation and experimental validation on an antenna structure model demonstrated that with a maximum deformation of 60 mm, the root mean square error (RMSE) was 0.72 mm, and the percentage error (PD) was 2.89%. This method achieved high-precision deformation reconstruction of antenna structures and shows promise for application in the design and manufacturing of deformable antenna structures.

Abstract:Flexible sensors capable of detecting both pressure and magnetic fields serve as the cornerstone for merging the real and virtual worlds and facilitate the advancement of wearable devices by providing a rapid response, user-friendly interface, and flexible human-machine interaction method. Constrained by the sensing structure, the arithmetic power of miniaturized wearable platforms and power consumption, these devices are currently difficult to realize the simultaneous sensing of multiple information such as human posture and position, as well as the deployment of complex models required for multi-information interaction. In order to bi-perceptive of high sensitivity and fast response, we proposed the magnetostrictive flexible sensor, which consists of the Co-Fe film and plane magnetic field sensing coils. The maximal sensitivity of the sensor is 22 mV/mm in the bending of 10~65 mm. The maximal sensitivity of the sensor is 1.78 mV/(kA/m) in the magnetic field of 1~11 kA/m. The sensor demonstrates excellent dynamic output stability, with sensitivity variations of less than 1.6% under different ρ and H conditions at frequencies of 1 to 4 Hz. By analyzing the input/output signal characteristics of the dual-sensing sensor, a sensing system supporting sensor signal processing and transmission is constructed to realize dual-sensing data acquisition. Adaptive fuzzy neural network is used to analyze the gesture and position information for gesture recognition, achieving a classification accuracy of 90.1% for 12 gestures that are commonly confused by traditional stress or bending sensors. In addition, the information interaction capability of the system is enhanced by combining the haptic feedback method, which helps the user to better grasp the effect of movement and relative position in the virtual environment by generating vibration feedback with different amplitudes and durations. The sensing system infers the user′s gesture movements and commands, and haptic feedback device response results, enabling a bidirectional information exchange between the user and the device.

Abstract:Flexible sensors capable of detecting both pressure and magnetic fields serve as the cornerstone for merging the real and virtual worlds and facilitate the advancement of wearable devices by providing a rapid response, user-friendly interface, and flexible humanmachine interaction method. Constrained by the sensing structure, the arithmetic power of miniaturized wearable platforms and power consumption, these devices are currently difficult to realize the simultaneous sensing of multiple information such as human posture and position, as well as the deployment of complex models required for multi-information interaction. In order to bi-perceptive of high sensitivity and fast response, we proposed the magnetostrictive flexible sensor, which consists of the Co-Fe film and plane magnetic field sensing coils. The maximal sensitivity of the sensor is 22 mV/ mm in the bending of 10~ 65 mm. The maximal sensitivity of the sensor is 1. 78 mV/ (kA/ m) in the magnetic field of 1 ~ 11 kA/ m. The sensor demonstrates excellent dynamic output stability, with sensitivity variations of less than 1. 6% under different ρ and H conditions at frequencies of 1 to 4 Hz. By analyzing the input / output signal characteristics of the dual-sensing sensor, a sensing system supporting sensor signal processing and transmission is constructed to realize dual-sensing data acquisition. Adaptive fuzzy neural network is used to analyze the gesture and position information for gesture recognition, achieving a classification accuracy of 90. 1% for 12 gestures that are commonly confused by traditional stress or bending sensors. In addition, the information interaction capability of the system is enhanced by combining the haptic feedback method, which helps the user to better grasp the effect of movement and relative position in the virtual environment by generating vibration feedback with different amplitudes and durations. The sensing system infers the user′s gesture movements and commands, and haptic feedback device response results, enabling a bidirectional information exchange between the user and the device.

Abstract:To address the problem of oil pollution reducing the accuracy and reliability of displacement sensor under service condition, this article studies the oil pollution error analysis and suppression method of grid scale based on an absolute time-grating sensor. Firstly, the measuring principle of the absolute time-grating sensor is described. Secondly, the mathematical theory model and the electric field simulation model of grid-scale oil pollution are formulated. The theoretical and simulation analysis shows that the uniform oil pollution has no effect on the sensor, the non-uniform oil pollution mainly introduces the first harmonic error, and the error increases with the increase of the thickness and width of the oil pollution. Finally, an experimental platform is set up to verify the correctness of the above theoretical analysis. Meanwhile, the differential structure is proposed, which effectively inhibits the first harmonic error and improves the anti-fouling ability of the sensor. The study of the error effect caused by oil pollution is of great significance to improve the environmental adaptability of sensors and also provides a theoretical basis for improving the long-term reliability of sensors and enhancing environmental adaptability.

Abstract:To address the problem of oil pollution reducing the accuracy and reliability of displacement sensor under service condition, this article studies the oil pollution error analysis and suppression method of grid scale based on an absolute time-grating sensor. Firstly, the measuring principle of the absolute time-grating sensor is described. Secondly, the mathematical theory model and the electric field simulation model of grid-scale oil pollution are formulated. The theoretical and simulation analysis shows that the uniform oil pollution has no effect on the sensor, the non-uniform oil pollution mainly introduces the first harmonic error, and the error increases with the increase of the thickness and width of the oil pollution. Finally, an experimental platform is set up to verify the correctness of the above theoretical analysis. Meanwhile, the differential structure is proposed, which effectively inhibits the first harmonic error and improves the anti-fouling ability of the sensor. The study of the error effect caused by oil pollution is of great significance to improve the environmental adaptability of sensors and also provides a theoretical basis for improving the long-term reliability of sensors and enhancing environmental adaptability.

Abstract:An attitude estimation method in the presence of unknown correspondences is proposed by utilizing inertial aid tracking. Unlike methods that optimize prior pose selection using inertial information at the data processing level, this approach combines image and inertial information at the data acquisition level, which could enhance the accuracy and robustness of attitude estimation and feature point matching. The proposed method introduces a new hybrid tracking approach that combines feature point tracking and motion vector constraint tracking. And an initial matching weight matrix for two-dimensional and three-dimensional point sets is established. An optimization objective function is formulated based on this weight matrix, and an improved soft assignment algorithm is employed to iteratively optimize the estimation of attitude and the correspondences between the 2D/ 3D point sets. Algorithm performance is tested by using a 2D precision turntable with cooperative stereo targets. The results show that the success rate of this method is 99% when the yaw/ pitch angle is ±50°, and the error of attitude estimation is less than 0. 15°. The single measurement time is approximately 17 ms, and the method maintains a high success rate even in visual occlusion K . eywords:monocular vision; attitude estimation; unknown correspondenc

Abstract:An attitude estimation method in the presence of unknown correspondences is proposed by utilizing inertial aid tracking. Unlike methods that optimize prior pose selection using inertial information at the data processing level, this approach combines image and inertial information at the data acquisition level, which could enhance the accuracy and robustness of attitude estimation and feature point matching. The proposed method introduces a new hybrid tracking approach that combines feature point tracking and motion vector constraint tracking. And an initial matching weight matrix for two-dimensional and three-dimensional point sets is established. An optimization objective function is formulated based on this weight matrix, and an improved soft assignment algorithm is employed to iteratively optimize the estimation of attitude and the correspondences between the 2D/3D point sets. Algorithm performance is tested by using a 2D precision turntable with cooperative stereo targets. The results show that the success rate of this method is 99% when the yaw/pitch angle is ±50°, and the error of attitude estimation is less than 0.15°. The single measurement time is approximately 17 ms, and the method maintains a high success rate even in visual occlusion.

Abstract:Slurry flotation level measurement is a key link in the production of beneficiation, the accuracy and stability of the constant current source in the measurement system directly affect the quality of the final product. Aiming at the shortcomings of the existing constant current source circuit, such as low output accuracy, high frequency characteristics and poor load capacity, a new type of highprecision differential constant current source circuit is designed in this paper. By incorporating compensation capacitors at both ends of the feedback loop and load, the circuit′s high-frequency characteristics are optimized, reducing the phase shift of the output signal. Additionally, the load characteristics are enhanced using a floating access load. As a result, the circuit′s output impedance and load capacity are improved. The simulation and practical testing of the circuit demonstrate its effectiveness. Measurement results indicate that the newly designed constant current source achieves an adjustable range of 100 Hz to 50 kHz and a constant current of 0 to 20 mA within a 0 to 4 kΩ range. The circuit′s frequency and load characteristics show significant improvement, with maximum relative errors of 0. 513% and 0. 378% , respectively, meeting the high accuracy requirements for liquid level measurement in the pulp flotation industry.

Abstract:Slurry flotation level measurement is a key link in the production of beneficiation, the accuracy and stability of the constant current source in the measurement system directly affect the quality of the final product. Aiming at the shortcomings of the existing constant current source circuit, such as low output accuracy, high frequency characteristics and poor load capacity, a new type of highprecision differential constant current source circuit is designed in this paper. By incorporating compensation capacitors at both ends of the feedback loop and load, the circuit′s highfrequency characteristics are optimized, reducing the phase shift of the output signal. Additionally, the load characteristics are enhanced using a floating access load. As a result, the circuit′s output impedance and load capacity are improved. The simulation and practical testing of the circuit demonstrate its effectiveness. Measurement results indicate that the newly designed constant current source achieves an adjustable range of 100 Hz to 50 kHz and a constant current of 0 to 20 mA within a 0 to 4 kΩ range. The circuit′s frequency and load characteristics show significant improvement, with maximum relative errors of 0513% and 0378%, respectively, meeting the high accuracy requirements for liquid level measurement in the pulp flotation industry.

Abstract:Multi-dimensional wireless power transmission is the key to the development of gastrointestinal capsule robots. To solve the problem that the functions of new capsule robots are increasing, and the traditional three-dimensional receiving coils take up too much internal space, this article proposes a three-dimensional combined transmitting coil that is able to generate a dynamic three-dimensional magnetic field by modulating the currents passing through each dimensional coil. Firstly, the working principle of the wireless energy supply system is introduced. Then, a finite element analysis of the spatial magnetic field generated by the transmitting system is implemented. Finally, experiments are conducted by using the constructed platform to evaluate the accuracy of the simulation results and determine the attitude of the receiving coil to sense the maximum receiving voltage. The experimental results show that the load-received energy exceeds 630 mW when the transmitter system generates equal three-dimensional magnetic field components at the center point. In addition, the positional stability of the edge region near the transmitter system exceeds 80%, which ensures that the capsule robot can operate smoothly.

Abstract:Multi-dimensional wireless power transmission is the key to the development of gastrointestinal capsule robots. To solve the problem that the functions of new capsule robots are increasing, and the traditional three-dimensional receiving coils take up too much internal space, this article proposes a three-dimensional combined transmitting coil that is able to generate a dynamic three-dimensional magnetic field by modulating the currents passing through each dimensional coil. Firstly, the working principle of the wireless energy supply system is introduced. Then, a finite element analysis of the spatial magnetic field generated by the transmitting system is implemented. Finally, experiments are conducted by using the constructed platform to evaluate the accuracy of the simulation results and determine the attitude of the receiving coil to sense the maximum receiving voltage. The experimental results show that the load-received energy exceeds 630 mW when the transmitter system generates equal three-dimensional magnetic field components at the center point. In addition, the positional stability of the edge region near the transmitter system exceeds 80% , which ensures that the capsule robot can operate smoothly.

Abstract:Mini / Micro-LED represents the next generation of display technology. As the physical size of Mini / Micro-LEDchips becomes smaller, fabrication yields have decreased while the degree of integration has significantly increased. Consequently, fast and accurate inspection of Mini / Micro-LED chips is crucial for industrial production. However, inspecting Mini / Micro-LED chips remains challenging due to their small size and dense distribution. The limited feature information from individual targets and the need for fast, easily deployable inspection algorithms add to these challenges. To address these issues, we designed a compressed attention detail-semantic complementary convolutional neural network (CADSC-CNN). By incorporating an encoder structure based on a self-attention mechanism into the feature fusion network, it becomes easier to acquire global information to complement the features of small targets. Additionally, the compression operation of self-attention reduces the model′s parameter count, thereby improving the detection rate. We validated the effectiveness of this method using a Mini / Micro-LED dataset collected by an industrial camera. Experiments demonstrated that this method achieves a mean average precision (mAP) rate of 95. 6% and a speed of 100. 6 frames per second.

Abstract:Mini/Micro-LED represents the next generation of display technology. As the physical size of Mini/Micro-LEDchips becomes smaller, fabrication yields have decreased while the degree of integration has significantly increased. Consequently, fast and accurate inspection of Mini/Micro-LED chips is crucial for industrial production. However, inspecting Mini/Micro-LED chips remains challenging due to their small size and dense distribution. The limited feature information from individual targets and the need for fast, easily deployable inspection algorithms add to these challenges. To address these issues, we designed a compressed attention detail-semantic complementary convolutional neural network (CADSC-CNN). By incorporating an encoder structure based on a self-attention mechanism into the feature fusion network, it becomes easier to acquire global information to complement the features of small targets. Additionally, the compression operation of self-attention reduces the model′s parameter count, thereby improving the detection rate. We validated the effectiveness of this method using a Mini/Micro-LED dataset collected by an industrial camera. Experiments demonstrated that this method achieves a mean average precision (mAP) rate of 95.6% and a speed of 100.6 frames per second.

Abstract:Joule balance is a self-developed device for quantized reproducing the kilogram in China. It is particularly important to achieve mass value transfer based on the joule balance after the kilogram is redefined. However, there is a problem with the accuracy of joule balance measurements due to the exposure of standard weights to the air environment during the transfer of mass values. To address this problem, based on the structural characteristics of the joule balance, a combined weight transfer scheme is designed and proposed, and a corresponding weight vacuum transfer device is developed, which can transfer standard weights from the joule balance to the vacuum container in a vacuum environment, ensuring the accuracy of mass value transfer. Then, based on the joule balance suspension system, a transfer accuracy model of the transfer device is formulated. Based on this model, the weight transfer x is obtained. The transmission errors of the y-axis are 0.011 6 μm and 0.009 2 μm, respectively, demonstrating the accuracy of the weight transmission process. Finally, it is verified through experiments that the weight vacuum transfer device could introduce an alignment error of no more than 10-9 orders of magnitude to the joule balance, and does not affect the precision alignment state of the joule balance.

Abstract:Joule balance is a self-developed device for quantized reproducing the kilogram in China. It is particularly important to achieve mass value transfer based on the joule balance after the kilogram is redefined. However, there is a problem with the accuracy of joule balance measurements due to the exposure of standard weights to the air environment during the transfer of mass values. To address this problem, based on the structural characteristics of the joule balance, a combined weight transfer scheme is designed and proposed, and a corresponding weight vacuum transfer device is developed, which can transfer standard weights from the joule balance to the vacuum container in a vacuum environment, ensuring the accuracy of mass value transfer. Then, based on the joule balance suspension system, a transfer accuracy model of the transfer device is formulated. Based on this model, the weight transfer x is obtained. The transmission errors of the y-axis are 0. 011 6 μm and 0. 009 2 μm, respectively, demonstrating the accuracy of the weight transmission process. Finally, it is verified through experiments that the weight vacuum transfer device could introduce an alignment error of no more than 10 -9 orders of magnitude to the joule balance, and does not affect the precision alignment state of the joule balance.

Abstract:To overcome the intense decrease in coupling coefficient and transfer efficiency caused by deviation and deflection of the coupling structure of the WPT system, this article proposes a wireless power transfer system with a asymmetric coupling structure based on rotating magnetic field coupling. The transmitter adopts a DQDD coil, and the receiver adopts a CD coil. Firstly, the rotation characteristics of the magnetic field excited by the transmitting coil and the coupling route of the receiving coil under deviation and deflection are analyzed. The LCC-S resonant topology is constructed, and the component configuration and the system efficiency expression are derived. Secondly, the effects of winding, position and size parameters of coupling structure on mutual inductance and coupling coefficient are established; and the mutual inductance and coupling coefficient of DQDD-CD coupling structure under deviation and deflection are given. The superiority of the proposed coupling structure in resisting deviation and deflection compared with the existing six structure is analyzed. Finally, a 500 W prototype is built with a transmission distance of 130 mm under the conditions of 50% horizontal deviation and 90° vertical deflection, which evaluates the anti-deflection performance of the DQDD-CD magnetic coupling mechanism and the transmission energy efficiency of the system.

Abstract:To overcome the intense decrease in coupling coefficient and transfer efficiency caused by deviation and deflection of the coupling structure of the WPT system, this article proposes a wireless power transfer system with a asymmetric coupling structure based on rotating magnetic field coupling. The transmitter adopts a DQDD coil, and the receiver adopts a CD coil. Firstly, the rotation characteristics of the magnetic field excited by the transmitting coil and the coupling route of the receiving coil under deviation and deflection are analyzed. The LCC-S resonant topology is constructed, and the component configuration and the system efficiency expression are derived. Secondly, the effects of winding, position and size parameters of coupling structure on mutual inductance and coupling coefficient are established; and the mutual inductance and coupling coefficient of DQDD-CD coupling structure under deviation and deflection are given. The superiority of the proposed coupling structure in resisting deviation and deflection compared with the existing six structure is analyzed. Finally, a 500 W prototype is built with a transmission distance of 130 mm under the conditions of 50% horizontal deviation and 90° vertical deflection, which evaluates the anti-deflection performance of the DQDD-CD magnetic coupling mechanism and the transmission energy efficiency of the system.

Abstract::To address the issue of unstable imaging performance of spatially modulated fully polarized imaging systems in different target scenes, this paper proposes a method for optimizing filter bandwidth calculation based on simulated interference watermark fringes. This method aims to ensure that the system maintains optimal performance across various target scenarios and provides theoretical guidance for selecting system imaging lenses. The approach involves generating simulated interference images by superimposing two-dimensional sine functions of varying sparsity, which represent simulated interference watermark fringes, onto intensity images. The fast Fourier transform and the frequency domain low-pass filtering algorithm are used to demodulate the simulated interference diagram, producing the total polarization image. The polarization image with the highest structural similarity to the intensity image is selected for further analysis. Additionally, an optimal filter bandwidth calculation method based on improved particle swarm optimization algorithm is proposed. The adaptive selection of the optimal filter bandwidth is achieved by introducing a vaccine extraction selection strategy and a simulated annealing mechanism. Combined with the selected image resolution, incident light wavelength and Savart polarizer single beam light offset in different target scenes, the optimal focal length of the imaging lens was calculated, and the theoretical selection was completed. In the experimental section, the stability of the spatial modulation-based full-polarization imaging system, built using the optimal filter bandwidth selection, is compared with that of the traditional experience-based selection. The experimental results show that the spatial modulation total polarization imaging system based on the optimal filter bandwidth has better performance. The area of spectral graph inversion is increased by 4. 16 times and the similarity of the image structure based on the optimal filter bandwidth selection is improved by 63% compared with the self-empirical selection, which significantly improves the polarization imaging quality of the system.

Abstract:To address the issue of unstable imaging performance of spatially modulated fully polarized imaging systems in different target scenes, this paper proposes a method for optimizing filter bandwidth calculation based on simulated interference watermark fringes. This method aims to ensure that the system maintains optimal performance across various target scenarios and provides theoretical guidance for selecting system imaging lenses. The approach involves generating simulated interference images by superimposing two-dimensional sine functions of varying sparsity, which represent simulated interference watermark fringes, onto intensity images. The fast Fourier transform and the frequency domain low-pass filtering algorithm are used to demodulate the simulated interference diagram, producing the total polarization image. The polarization image with the highest structural similarity to the intensity image is selected for further analysis. Additionally, an optimal filter bandwidth calculation method based on improved particle swarm optimization algorithm is proposed. The adaptive selection of the optimal filter bandwidth is achieved by introducing a vaccine extraction selection strategy and a simulated annealing mechanism. Combined with the selected image resolution, incident light wavelength and Savart polarizer single beam light offset in different target scenes, the optimal focal length of the imaging lens was calculated, and the theoretical selection was completed. In the experimental section, the stability of the spatial modulation-based full-polarization imaging system, built using the optimal filter bandwidth selection, is compared with that of the traditional experience-based selection. The experimental results show that the spatial modulation total polarization imaging system based on the optimal filter bandwidth has better performance. The area of spectral graph inversion is increased by 4.16 times and the similarity of the image structure based on the optimal filter bandwidth selection is improved by 63% compared with the self-empirical selection, which significantly improves the polarization imaging quality of the system.

Abstract:To satisfy the application of the retractable optical system in the space environment, this article takes the Schlieren optical system in a space combustion optical experiment equipment as an example. The traditional mirror bonding process is susceptible to temperature change and the image quality of the optical system is affected. To address this problem, a method of adhesive layer bonding design and quantitative analysis of the mirror assembly is proposed, and the precision measurement of its space attitude is studied. Epoxy resin adhesive is used as the main adhesive of the mirror assembly, and the theoretical calculation equation of the adhesive layer is improved and optimized under the constraint condition of non-thermal. Through multi-objective simulation analysis and data fitting, the appropriate thickness of adhesive layer is selected. The coaxial assembly of mirror and frame is realized by using center deviation measuring instrument and tool measuring method. On this basis, a coordinate system based on theodolite measurement is established by using the coordinate transformation method, and the corresponding relationship between the pitch and azimuth deviation of the plane mirror assembly and the measured values of the theodolite is derived. The final environmental test results show that this method can be used to realize the work of Schlieren optical system in the space complex environment. The accuracy of the plane mirror adhesion is 0. 023λ, the angle between the plane mirror assembly and the optical axis of the optical system is better than 10″, and the imaging of the optical device is clear when the temperature range is -5℃ ~ +45℃ . This method has also been applied to other research projects.

Abstract:To satisfy the application of the retractable optical system in the space environment, this article takes the Schlieren optical system in a space combustion optical experiment equipment as an example. The traditional mirror bonding process is susceptible to temperature change and the image quality of the optical system is affected. To address this problem, a method of adhesive layer bonding design and quantitative analysis of the mirror assembly is proposed, and the precision measurement of its space attitude is studied. Epoxy resin adhesive is used as the main adhesive of the mirror assembly, and the theoretical calculation equation of the adhesive layer is improved and optimized under the constraint condition of non-thermal. Through multi-objective simulation analysis and data fitting, the appropriate thickness of adhesive layer is selected. The coaxial assembly of mirror and frame is realized by using center deviation measuring instrument and tool measuring method. On this basis, a coordinate system based on theodolite measurement is established by using the coordinate transformation method, and the corresponding relationship between the pitch and azimuth deviation of the plane mirror assembly and the measured values of the theodolite is derived. The final environmental test results show that this method can be used to realize the work of Schlieren optical system in the space complex environment. The accuracy of the plane mirror adhesion is 0.023λ, the angle between the plane mirror assembly and the optical axis of the optical system is better than 10″, and the imaging of the optical device is clear when the temperature range is -5℃~+45℃. This method has also been applied to other research projects.

Abstract:Data-driven methods are commonly used for thermal error modeling, but the open-loop serial structure without mechanistic support makes it challenging to ensure model robustness under new operating conditions, leading to unreliable prediction performance. This paper introduces a semi-closed-loop spindle thermal error modeling approach based on dataset reconstruction. The original modeling batches are sorted and screened according to the ambient temperature of both the prediction and modeling batches. The modeling dataset is then reconstructed, and a semi-closed-loop thermal error model is developed. This method was applied to predict thermal errors in a lathe spindle, achieving root mean square errors of 1.7 μm, 1.7 μm, and 0.9 μm in three test sets with the reconstructed models. Compared to conventional models, accuracy improved by 29.2%, 39.3%, and 64.0%, respectively. This approach introduces a feedback loop to the existing serial open-loop thermal error modeling, offering significant potential for enhancing the performance of thermal error models.

Abstract:Data-driven methods are commonly used for thermal error modeling, but the open-loop serial structure without mechanistic support makes it challenging to ensure model robustness under new operating conditions, leading to unreliable prediction performance. This paper introduces a semi-closed-loop spindle thermal error modeling approach based on dataset reconstruction. The original modeling batches are sorted and screened according to the ambient temperature of both the prediction and modeling batches. The modeling dataset is then reconstructed, and a semi-closed-loop thermal error model is developed. This method was applied to predict thermal errors in a lathe spindle, achieving root mean square errors of 1. 7 μm, 1. 7 μm, and 0. 9 μm in three test sets with the reconstructed models. Compared to conventional models, accuracy improved by 29. 2% , 39. 3% , and 64. 0% , respectively. This approach introduces a feedback loop to the existing serial open-loop thermal error modeling, offering significant potential for enhancing the performance of thermal error models.

Abstract:To address the problems of poor stability, incomplete information, and low efficiency of geometric parameter measurement in the production of spiral point taps, a fast non-contact measurement method based on line laser scanning is proposed. Firstly, the noncontact measurement kinematics model for spiral point taps is formulated, which is based on the homogeneous transformation theory. After the preprocessing of measurement data, the full-information surface reconstruction of spiral point taps is realized based on multiview splicing. Then, a point cloud segmentation method and an edge line extraction algorithm considering the surface characteristics of spiral point taps are proposed. On this basis, the geometric parameters, such as the rake angle, spiral pointed angle, core diameter, land width, etc. of taps are calculated comprehensively and efficiently. Finally, the measurement repeatability and comparison experiments are implemented on the CNC machine tool for the spiral point tap in the production process. Results show that the average measurement efficiency of the proposed method increases by 3. 88 times and the maximum relative measurement error is less than 2% , which evaluates the feasibility of the measurement system and has good measurement repeatability and accuracy

Abstract:To address the problems of poor stability, incomplete information, and low efficiency of geometric parameter measurement in the production of spiral point taps, a fast non-contact measurement method based on line laser scanning is proposed. Firstly, the non-contact measurement kinematics model for spiral point taps is formulated, which is based on the homogeneous transformation theory. After the preprocessing of measurement data, the full-information surface reconstruction of spiral point taps is realized based on multi-view splicing. Then, a point cloud segmentation method and an edge line extraction algorithm considering the surface characteristics of spiral point taps are proposed. On this basis, the geometric parameters, such as the rake angle, spiral pointed angle, core diameter, land width, etc. of taps are calculated comprehensively and efficiently. Finally, the measurement repeatability and comparison experiments are implemented on the CNC machine tool for the spiral point tap in the production process. Results show that the average measurement efficiency of the proposed method increases by 3.88 times and the maximum relative measurement error is less than 2%, which evaluates the feasibility of the measurement system and has good measurement repeatability and accuracy.

Abstract:On-machine measurement can be used for rea-time measurement during machining, which can effectively improve the machining quality and measurement efficiency of face gear. Considering the influence of the pre-travel error of face gear on-machine measurement on the tooth surface accuracy and the low tooth surface matching accuracy, an on-machine measurement strategy based on worm grinding wheel gear grinding machine, which takes various error influencing factors as input and the comprehensive pre-travel error of probe as output. A comprehensive pre-travel error prediction model based on PSO-CNN neural network is formulated to complete error compensation. A precise tooth surface matching method is proposed, and a six-parameter optimization model is established to match the measured tooth surface with the theoretical tooth surface to obtain the tooth surface error. Finally, the experimental results show that the accuracy of the left tooth surface is improved by 61.33%, and the accuracy of the right tooth surface is improved by 71.15%. The results of on-machine measurement are basically consistent with those of Klingeln Berg measuring instrument, which meet the requirements of on-machine measurement accuracy of face gear.

Abstract:On-machine measurement can be used for real-time measurement during machining, which can effectively improve the machining quality and measurement efficiency of face gear. Considering the influence of the pre-travel error of face gear on-machine measurement on the tooth surface accuracy and the low tooth surface matching accuracy, an on-machine measurement strategy based on worm grinding wheel gear grinding machine, which takes various error influencing factors as input and the comprehensive pre-travel error of probe as output. A comprehensive pre-travel error prediction model based on PSO-CNN neural network is formulated to complete error compensation. A precise tooth surface matching method is proposed, and a six-parameter optimization model is established to match the measured tooth surface with the theoretical tooth surface to obtain the tooth surface error. Finally, the experimental results show that the accuracy of the left tooth surface is improved by 61. 33% , and the accuracy of the right tooth surface is improved by 71. 15% . The results of on-machine measurement are basically consistent with those of Klingeln Berg measuring instrument, which meet the requirements of on-machine measurement accuracy of face gear.

Abstract:The current-feedback instrumentation amplifier chip is extensively employed in weak signal detection due to its high precision and high common-mode rejection ratio. Conventional CFIA utilizes chopping techniques to reduce 1 / f noise and offset voltage, enhancing the amplifier′s precision. However, the resultant ripple significantly limits the accuracy improvement. Addressing this, an innovative adaptive CLK ripple reduction loop CFIA ARCFIA is proposed. It employs a ripple reduction loop RRL to reduce the ripple in conventional chopping amplifiers. In addition, with the aid of an Adaptive CLK ACLK, it reduces the power spectral density of the inputreferred noise in chopping switch. Experimental results show that ARCFIA achieves a low offset voltage below 1. 4 μV and an inputreferred noise of 17. 2 nV/ Hz , while the ripple is reduced to a level below the ARCFIA′s input-referred noise. This reduction in offset, noise, and ripple leads to improved accuracy. Furthermore, ARCFIA shows potential for application in high-precision measurement systems within complex environments.

Abstract:The current-feedback instrumentation amplifier chip is extensively employed in weak signal detection due to its high precision and high common-mode rejection ratio. Conventional CFIA utilizes chopping techniques to reduce 1/f noise and offset voltage, enhancing the amplifier′s precision. However, the resultant ripple significantly limits the accuracy improvement. Addressing this, an innovative adaptive CLK ripple reduction loop CFIA ARCFIA is proposed. It employs a ripple reduction loop RRL to reduce the ripple in conventional chopping amplifiers. In addition, with the aid of an Adaptive CLK ACLK, it reduces the power spectral density of the input-referred noise in chopping switch. Experimental results show that ARCFIA achieves a low offset voltage below 1.4 μV and an input-referred noise of 17.2 nV/Hz, while the ripple is reduced to a level below the ARCFIA′s input-referred noise. This reduction in offset, noise, and ripple leads to improved accuracy. Furthermore, ARCFIA shows potential for application in high-precision measurement systems within complex environments.

Abstract:The traditional binocular stereo vision measurement is based on stereo rectification for 3D reconstruction. The measurement accuracy is greatly influenced by the calibration parameter accuracy, interpolation accuracy and binocular sensor structure. When measuring the deformation of surface, especially large curvature ROI, the stereo rectification process could cause the loss or over-fitting of the non-uniform deformation information in the stereoscopic image pairs, which further affects the measurement accuracy. To address this issue, a high-precision stereo matching method is proposed based on reprojection and 3D-DIC, which is suitable for surface deformation measurement without stereo rectification or epipolar geometric constraint correction. Meanwhile, this method can also be extended to high-precision matching of time series image pairs containing non-uniform deformation. Specifically, this article proposes the estimation method of second-order deformation parameters, the growth matching strategy of second-order deformation parameters, and the global stereo matching strategy and the time series matching strategy. Furthermore, the 3D reconstruction method of feature points independent of stereo rectification and epi-polar correction, and the calculation method of global deformation field and local strain field are given. Experiments show that proposed method can achieve high precision deformation measurement of a curved surface, and the measuring system can achieve an average measurement error of less than 1 μm in a certain depth of field.

Abstract:The traditional binocular stereo vision measurement is based on stereo rectification for 3D reconstruction. The measurement accuracy is greatly influenced by the calibration parameter accuracy, interpolation accuracy and binocular sensor structure. When measuring the deformation of surface, especially large curvature ROI, the stereo rectification process could cause the loss or over-fitting of the non-uniform deformation information in the stereoscopic image pairs, which further affects the measurement accuracy. To address this issue, a high-precision stereo matching method is proposed based on reprojection and 3D-DIC, which is suitable for surface deformation measurement without stereo rectification or epi-polar geometric constraint correction. Meanwhile, this method can also be extended to high-precision matching of time series image pairs containing non-uniform deformation. Specifically, this article proposes the estimation method of second-order deformation parameters, the growth matching strategy of second-order deformation parameters, and the global stereo matching strategy and the time series matching strategy. Furthermore, the 3D reconstruction method of feature points independent of stereo rectification and epi-polar correction, and the calculation method of global deformation field and local strain field are given. Experiments show that proposed method can achieve high precision deformation measurement of a curved surface, and the measuring system can achieve an average measurement error of less than 1 μm in a certain depth of field.

Abstract:Due to its simple structure and no need of coupling, the electromagnetic acoustic transducer ( EMAT) holds significant potential in the field of ultrasonic nondestructive testing. However, traditional permanent magnet iron shear wave EMATs have several drawbacks, including low energy conversion efficiency, inconsistency in the generated ultrasonic modes, strong magnetic attraction, and difficulty in movement. These issues not only impact the accuracy of test results but also limit the range of applications. This study introduces a new type of electromagnet shear wave transducer, combining the features of permanent magnet Halbach EMATs and hollow electromagnet EMATs. This novel design enhances the magnetic field on one side and allows for adjustable bias magnetic field intensity. The study employs an orthogonal experimental method to optimize the structural parameters of the transducer′s excitation magnet and eddy current coil, resulting in improved echo signal amplitude. The findings indicate that the optimized EMAT significantly enhances the intensity and distribution range of the vertical magnetic field on the specimen surface while greatly reducing the horizontal magnetic field, thereby more effectively generating pure shear waves.

Abstract:Due to its simple structure and no need of coupling, the electromagnetic acoustic transducer (EMAT) holds significant potential in the field of ultrasonic nondestructive testing. However, traditional permanent magnet iron shear wave EMATs have several drawbacks, including low energy conversion efficiency, inconsistency in the generated ultrasonic modes, strong magnetic attraction, and difficulty in movement. These issues not only impact the accuracy of test results but also limit the range of applications. This study introduces a new type of electromagnet shear wave transducer, combining the features of permanent magnet Halbach EMATs and hollow electromagnet EMATs. This novel design enhances the magnetic field on one side and allows for adjustable bias magnetic field intensity. The study employs an orthogonal experimental method to optimize the structural parameters of the transducer′s excitation magnet and eddy current coil, resulting in improved echo signal amplitude. The findings indicate that the optimized EMAT significantly enhances the intensity and distribution range of the vertical magnetic field on the specimen surface while greatly reducing the horizontal magnetic field, thereby more effectively generating pure shear waves.

Abstract:This study proposes a method for damage probability distribution imaging of shear horizontal (SH) waves in thin plate structures by introducing a denoising factor, aiming to improve the accuracy of damage detection in complex environments. Addressing factors such as thin plate deformation, environmental interference, system errors, and human operation in guided wave structural health monitoring, this method reduces the interference of noise signals by introducing a denoising factor. To mitigate the impact of these noise signals, this method introduces a denoising factor and employs an adaptive threshold based on local peak values to isolate damage, thereby significantly enhancing imaging effectiveness. The propagation characteristics of SH-waves in thin plate structures are analyzed, and experiments are conducted on steel plates for validation. Results demonstrate that compared to traditional RAPID imaging methods, this approach enables more accurate damage localization. Furthermore, the study explores the advantages of denoising factorintroduced tomography imaging in reducing noise influence and detecting multiple damages, providing theoretical and engineering support for SH wave based thin plate structure damage detection.

Abstract:This study proposes a method for damage probability distribution imaging of shear horizontal ( SH) waves in thin plate structures by introducing a denoising factor, aiming to improve the accuracy of damage detection in complex environments. Addressing factors such as thin plate deformation, environmental interference, system errors, and human operation in guided wave structural health monitoring, this method reduces the interference of noise signals by introducing a denoising factor. To mitigate the impact of these noise signals, this method introduces a denoising factor and employs an adaptive threshold based on local peak values to isolate damage, thereby significantly enhancing imaging effectiveness. The propagation characteristics of SH-waves in thin plate structures are analyzed, and experiments are conducted on steel plates for validation. Results demonstrate that compared to traditional RAPID imaging methods, this approach enables more accurate damage localization. Furthermore, the study explores the advantages of denoising factor-introduced tomography imaging in reducing noise influence and detecting multiple damages, providing theoretical and engineering support for SH wave based thin plate structure damage detection.

Abstract:Acoustic temperature measurement of boilers relies heavily on the estimation accuracy and real-time performance of acoustic propagation time delay. To address the limitation of traditional methods, which can only estimate the delay with integer times of the sampling points, this paper proposes a high-precision multiplexed delay estimation method based on phase compensation. This method constructs a sinusoidal-chirp composite signal, and realizes high-precision delay estimation by combining generalized cross-correlation and phase estimation with the all-phase Fourier transform. Through simulation and experimental verification, the accuracy of the signal constructed in this paper matches that of other commonly used signals when using the generalized quadratic inter-correlation method. With phase compensation, it has higher accuracy and noise immunity, improving precision by more than 8. 5 times, and showing minimal impact form sampling frequency. This method maintains consistent performance in multi-channel synchronous time delay estimation using frequency division multiplexing, reducing the measurement time to 1 / 8 of traditional methods. It provides an effective solution to achieve faster and more accurate acoustic temperature measurements.

Abstract:Acoustic temperature measurement of boilers relies heavily on the estimation accuracy and real-time performance of acoustic propagation time delay. To address the limitation of traditional methods, which can only estimate the delay with integer times of the sampling points, this paper proposes a high-precision multiplexed delay estimation method based on phase compensation. This method constructs a sinusoidal-chirp composite signal, and realizes high-precision delay estimation by combining generalized cross-correlation and phase estimation with the all-phase Fourier transform. Through simulation and experimental verification, the accuracy of the signal constructed in this paper matches that of other commonly used signals when using the generalized quadratic inter-correlation method. With phase compensation, it has higher accuracy and noise immunity, improving precision by more than 8.5 times, and showing minimal impact form sampling frequency. This method maintains consistent performance in multi-channel synchronous time delay estimation using frequency division multiplexing, reducing the measurement time to 1/8 of traditional methods. It provides an effective solution to achieve faster and more accurate acoustic temperature measurements.

Abstract:To address the issue of low accuracy and poor robustness in single-feature sound source tracking under strong indoor reverberation and low signal-to-noise ratio (SNR), a robust tracking algorithm using multi-feature optimization mechanism is presented in this paper. This algorithm establishes a multi-feature optimization mechanism based on a time-delay estimation multi-hypothesis model, overcoming the poor localization performance of single-feature tracking in reverberant noise environments. Moreover, To enhance the robustness of the multi-feature optimization mechanism against random movements of the speaker, we introduce an improved Interacting Multiple Model (IMM) particle filter algorithm. By real-time adjustment of model noise variance and model probability, the robustness of the multi-feature optimization mechanism is improved. Simulation analysis and actual test results indicate that the average root mean square error (RMSE) of the position is reduced by approximately 12% using the proposed algorithm, compared with the existing literature, under the multi-feature optimization mechanism. Based on the improved IMM algorithm, the average RMSE of the position is reduced by nearly 89.6% through the proposed algorithm, compared with the other algorithms. The proposed algorithm significantly eliminates the adverse effects of reverberation and noise, and improves the accuracy and robustness of sound source localization and tracking.

Abstract:To address the issue of low accuracy and poor robustness in single-feature sound source tracking under strong indoor reverberation and low signal-to-noise ratio (SNR), a robust tracking algorithm using multi-feature optimization mechanism is presented in this paper. This algorithm establishes a multi-feature optimization mechanism based on a time-delay estimation multi-hypothesis model, overcoming the poor localization performance of single-feature tracking in reverberant noise environments. Moreover, To enhance the robustness of the multi-feature optimization mechanism against random movements of the speaker, we introduce an improved Interacting Multiple Model (IMM) particle filter algorithm. By real-time adjustment of model noise variance and model probability, the robustness of the multi-feature optimization mechanism is improved. Simulation analysis and actual test results indicate that the average root mean square error (RMSE) of the position is reduced by approximately 12% using the proposed algorithm, compared with the existing literature, under the multi-feature optimization mechanism. Based on the improved IMM algorithm, the average RMSE of the position is reduced by nearly 89. 6% through the proposed algorithm, compared with the other algorithms. The proposed algorithm significantly eliminates the adverse effects of reverberation and noise, and improves the accuracy and robustness of sound source localization and tracking.

Abstract:To address the issue of slowly varying broadband background noise and channel effects caused by vibration of the target in laser-coherent speech detection, this paper proposes a speech enhancement method for specific speakers based on an analysis-resynthesis framework. This method first extracts the features from the observed signal: pitch, voiced speech probability, and MCEP coefficients, where MCEP coefficients represent the spectral envelope features which can capture the shape of the spectral envelope. A GMM trained by speech features of the corresponding speaker is used to help estimate the spectral envelope features of the clean speech from the spectral envelope features of the observed speech, and then the speech signal is resynthesized by combining it with pitch and voiced speech probability estimated from the observed speech to achieve speech enhancement. The estimation of noise and channel parameters is achieved by adaptation, which maximize the posterior probability of the observed speech′s spectral envelope features, and then the estimation of the clean speech spectral envelope features is obtained by MMSE estimation. Both synthesized signal experiments and actual signal acquisition experiments verify the denoising and equalization capabilities of the algorithm in laser coherent speech detection scenarios.

Abstract:To address the issue of slowly varying broadband background noise and channel effects caused by vibration of the target in laser-coherent speech detection, this paper proposes a speech enhancement method for specific speakers based on an analysis-re-synthesis framework. This method first extracts the features from the observed signal: pitch, voiced speech probability, and MCEP coefficients, where MCEP coefficients represent the spectral envelope features which can capture the shape of the spectral envelope. A GMM trained by speech features of the corresponding speaker is used to help estimate the spectral envelope features of the clean speech from the spectral envelope features of the observed speech, and then the speech signal is resynthesized by combining it with pitch and voiced speech probability estimated from the observed speech to achieve speech enhancement. The estimation of noise and channel parameters is achieved by adaptation, which maximize the posterior probability of the observed speech′s spectral envelope features, and then the estimation of the clean speech spectral envelope features is obtained by MMSE estimation. Both synthesized signal experiments and actual signal acquisition experiments verify the denoising and equalization capabilities of the algorithm in laser coherent speech detection scenarios.When the speech signal is detected by laser doppler vibrometer, there will be slowly varying broadband background noise and channel effects caused by the vibration measurement target. Thus, we propose a speech enhancement method for a specific speaker based on the analysis-resynthesis framework to enhance that distorted signal. This method first extracts the features from the observed signal: pitch, voiced speech probability, and MCEP coefficients, where MCEP coefficients are the spectral envelope features which can capture the shape of the spectral envelope. A GMM trained by speech features of the corresponding person is used to help estimate the spectral envelope features of the clean speech from the spectral envelope features of the observed speech, and then the speech signal is resynthesized by combining it with pitch and voiced speech probability estimated from the observed speech to achieve speech enhancement. The estimation of noise and channel parameters is achieved by adaptation, which maximize the posterior probability of the observed speech′s spectral envelope features, and then the estimation of the clean speech spectral envelope features is obtained by MMSE estimation. Synthetic signal experiments and actual signal acquisition experiments verify the denoising and equalization capabilities of the algorithm in laser coherent speech detection scenario.