Abstract:Abstract:Fluorescencebased sensing technology belongs to the multidiscipline fusion of sensors and transducers. Especially, research on physical quantity is in the startup stage. The principles and methods of fluorescence temperature/force/displacement/humiditysensing techniques are introducted. And some examples are illustrated, including our researches. Fluorescence lifetime, fluorescence intensity quenching, fluorescence intensity ratio, fluorescence spectral shift, and fluorescence colormetric analysis processes are discussed. The typical sensing properties are compared with other techniques. A particular note is that fluorescence lifetime is the other side of fluorescence intensity quenching. Data processing is introduced, which focuses on how to improve the sensing precision with a novel spectral characteristic, named as “emission band barycenter”. At the end, the future development and applications are provided.

Abstract:Abstract:The triaxial MEMS gyroscope in the magneto inertial navigation system (MINS) of unmanned aerial vehicle (UAV) needs to be calibrated. To solve this problem, a cross calibration method is proposed, which is based on the relationship between the angular velocity and the time derivative of a vector to calibrate errors of gyroscope. The time derivative of a constant vector in the navigation coordination frame can be expressed by the cross product of the vector itself and the angular velocity of the aircraftbody coordinate frame. The cross calibration method is derived from the above principles, which can calibrate the triaxial gyroscope efficiently without precision equipment. Numerical simulation results show that both the integral form and differential form of the cross product calibration method can effectively identify and compensate the error coefficients of gyroscope. And promising calibration results can be achieved under the influence of various factors. Experimental results on the gyroscope of the MINS module show that the accuracy of the proposed method can reach 02279°/s, which is close to the conventional method based on the rate table. The calibrated gyroscope data are combined with the secondorder complementary filtering algorithm in the flight control of a rotor UAV. The angle deviation is controlled within 08° in the fixedpoint hover state, which is conducive to the infield calibration of UAV and the measurement of the attitude data in real flight.

Abstract:Abstract:The magnetoimpedance effect has the evenorder nonlinearity. To solve this problem, a selfbalanced detection method is proposed. By applying AC bias field and using phase sensitive detection, the output of sensor has the feature of odd symmetry. The direction of external field can be detected without DC bias. Then, a proportionalintegral (PI) controller is utilized to form a closedloop detection system. When the system reaches the steady state, the feedback field and the external field are counteracted with each other. And the selfbalanced detection will be implemented. In this way, the nonlinearity of the sensor is eliminated. Experiments are implemented on a thinfilminductive magnetic sensor. Results show that the output of the selfbalanced detection method has good linearity, and the nonlinearity is only 082%. The measurement range is enlarged double. Even under different AC bias, the output characteristics is consistent and the correlation coefficients are all larger than 0999 9. The selfbalanced detection with AC bias is suitable for all nonlinear magnetic sensors. Different kinds of nonlinear characteristic can be eliminated by the proposed method and the highlinear output can be achieved.

Abstract:Abstract:With the development of nanofilm deposition technology, the transient temperature measurement based on thin film thermocouple has been widely applied in various applications. To realize the application of thin film thermocouple in milling, a method for preparing high performance thin film thermocouple is proposed. NiCr/NiSi thin film thermocouple is prepared on a quartz substrate by DC pulse magnetron sputtering technology. It is annealed at different temperatures in an argon atmosphere. The influences of annealing on the comprehensive property of the thin film thermocouple are studied. Experimental results show that uniformity and conductivity of functional films annealed at 500℃ are improved significantly. The Seebeck coefficient of thin film thermocouple is increased from 363 μV/℃ before annealing to the maximum value of 405 μV/℃. The performance of temperature measurement remains stable after continuous thermal shock and high temperature insulation for 6 hours. The research result of highperformance thin film thermocouple is used in the development of temperature measurement milling cutter and TC4 orthogonal milling. The prediction formula of TC4 milling temperature is obtained. The proposed method provides a feasible solution for the temperature measurement of of rotary processing.

Abstract:Abstract:Finite element simulation and experiment methods are employed to analyze the detection ability of flexible eddy current sensor for detecting the orientation angle and depth of a simulated crack defect when the circular coil is in different bending angles. The obtained results show that the bending of the circular coil leads the isotropic eddy current field to change to unidirectional eddy current field. No matter the flexible eddy current sensor works under self induction mode or mutual induction mode, the sensitivity of the flexible eddy current sensor to the crack faults with different orientation angles decreases. For instance, when the bending angle of the coil is 30°, the sensitivity of the flexible eddy current sensor to the crack with an orientation angle of 90° in self and mutual induction modes reduces by 7% and 45%, respectively. In addition, the coil bending causes that the recognition ability of the flexible eddy current sensor to the crack orientation angle and depth monotonously decreases as the bending angle of the coil increases. Under mutualinduction mode the flexible eddy current sensor achieves better recognition ability to crack orientation angle and depth compared with the case under the selfinduction mode. The influence of the coil bending occurred in the shape matching between the coil and curved surface on the performance of the sensor can not be ignored. A reasonable coil bending angle range should be selected when designing the flexible eddy current sensor.

Abstract:Abstract:The development of smart grid makes the data obtained from the grid gradually increasing. In order to obtain useful information from multidimensional big data and accurately predict the shortterm power load, this paper proposes a shortterm power load forecasting method based on dimension reduction with improved deep sparse autoencoder (IDSAE) and extreme learning machine (ELM) optimized with fruit fly optimization algorithm (FOA). Adding L1 regularization to the deep sparse autoencoder （DSAE） can induce better sparsity, and the improved deep sparse autoencoder is used to reduce the dimensionality of highdimensional data that affects the accuracy of power load prediction, which eliminates the multicollinearity among the indexes and realizes compression coding from highdimensional data to lowdimensional space. The fruit fly optimization algorithm (FOA) is used to optimize the weights and thresholds of the extreme learning machine (ELM), and the optimal weights and thresholds are obtained, which can overcome the shortcomings of low prediction accuracy caused by the extreme learning machine randomly selecting the weights and thresholds. In this paper, the meteorological factors are first dimension reduced by the IDSAE to obtain the sparse comprehensive meteorological factor characteristic indexes, and the coordinated power load data are used as the input vector of the FOA optimized ELM prediction model to perform power load prediction. The comparison experiments with DSAEFOAELM, DSAEELM, IDSAEELM and other models prove that the proposed prediction model can effectively improve the prediction accuracy, and the improved accuracy is about 8% after calculation.

Abstract:Abstract:In order to solve the problem that due to the lack of anomalous sensing data, it is difficult for the quadrotor unmanned aerial vehicle (UAV) to implement the motion condition assessment of flight control system, this paper proposes a method to generate anomalous attitude data based on physical simulation model. Firstly, the moving coordinate system of quadrotor unmanned aerial vehicle is defined and NewtonEulerian formula is used to establish the UAV motion equation. In this way, the control loop of the flight control system is designed. The physical simulation model of the flight control system is established with Simulink software, which provides the experiment environment for generating anomalous sensing data. Secondly, the real attitude sensing data of the quadrotor UAV are utilized to verify the applicability of the simulation model, and through abnormal injection the anomalous attitude data are generated. Finally, taking the anomaly detection method based on principle component analysis (PCA) as an example, the application effect of the generated anomalous sensing data is evaluated. Experiment results show that the method proposed in this paper can effectively generate two kinds of anomalous attitude sensing data with constant bias and drift. The anomaly detection results of PCA method show that false positive ratio is 2%～74% and accuracy is 73%～94%. Therefore, the proposed sensing data generation method can provide the corresponding data support for improving the performance of anomaly detection method.

Abstract:Abstract:Based on the analysis of large amounts of time series data collected by supervisory control and data acquisition system (SCADA) in wind turbines, a wind turbine online condition monitoring approach based on generative adversarial network (GAN) is proposed. Firstly, a data set that has the same dimension with the SCADA data is generated with the generative model. Secondly, the generated SCADA data and the real SCADA data are used to optimize and train the GAN model. After training, the obtained discriminative model in GAN is used for distinguishing the health condition of wind turbines. Finally, the proposed approach was used to analyze the SCADA data of a healthy and a faulty wind turbines. The result shows that the GANbased approach can effectively monitor the online operation condition of the wind turbines, it can detect the anomalies of the faulty wind turbine 5 days earlier than the SCADA system. When the wind turbine works normally, the number of false alarms reported by the GAN approach is less than other approaches (such as Mahalanobis distance, principal component analysis, deep neural network and support vector machine). When the wind turbine fails, the GANbased approach can detect more abnormal samples than other approaches.

Abstract:Abstract:To learn partbased representation of data and enhance sparseness, this study demonstrates the embedding of nonnegativity constraints in the deep network. A state recognition method for rolling bearing is proposed based on the deep autoencoder neural network with nonnegative constrains. Multiple autoencoders and a classification layer are stacked to formulate an integrated model for feature selflearning and state recognition. The bearing vibration timefrequency spectrogram is taken as input, and the model is optimized by combining unsupervised layerwise pretraining and supervised finetuning. Both of them are with the nonnegativity constraints embedding. The deep network encodes and extracts the intrinsic feature of data layer by layer. The nonnegative constrains and denoising encoding improve the partbased representation ability of deep network. And the influence of condition variation and noise interference is decreased. The proposed method is applied to the vibration data analysis of two kinds of rolling bearings. The average recognition accuracy of four different state bearings under variable conditions and eight different state bearings under constant conditions are 9799% and 9732%, respectively. The average recognition accuracy of bearings with different retainer wear levels is 9564%. Meanwhile, the proposed method shows good antinoise capability under different levels of noise.

Abstract:Abstract:Due to the high complexity, it is hard to recognize hydraulic signals. To solve this problem, a deep neural network is formulated for recognition of hydraulic pump leakage status, which is based on the stacked sparse autoencoder and Softmax. The lowlevel features are extracted by the wavelet transform and the HilbertHuang transform. These features are put into the deep neural network. Through the layerbylayer learning of stacked sparse autoencoder, the lowlevel features are optimized and the highlevel features are obtained. Then, Softmax is used to recognize other features. Experimental results show that the stacked sparse autoencoder can effectively extract the highlevel features of hydraulic pump leakage status. The formulated deep neural network can distinguish the pump leakage status and the recognition accuracy is 976%. In addition, compared with extreme learning machine, support vector machine, convolutional neural networks and long shortterm memory, the deep neural network has better recognition effectiveness.

Abstract:Abstract:Inertial navigation system needs initial alignment before normal operation, cubature Kalman filter (CKF) is a common algorithm for nonlinear initial alignment. Aiming at the problems that accuracy decline or even divergence appear in conventional cubature Kalman filter under the conditions of inaccurate filtering model and nonGaussian observation noise interference, a robust fading CKF algorithm is proposed in this paper. Multiple fading factors are introduced to adjust the observation noise covariance matrix or state prediction covariance matrix. A filter state Chisquare test method based on the statistical characteristics of filtering residual sequence is designed to check the filter state, and determine the introducing means of the fading factors autonomously, which makes the introduction of the fading factors is more reasonable. Experiment results show that the proposed algorithm can maintain strong robustness and adaptability even under the conditions of inaccurate system modeling and abnormal nonGaussian observation noise interference. The attitude misalignment error is about 001 ° and the yaw misalignment error is less than 01°.

Abstract:Abstract:The sources of the magnetic beacon navigation and positioning system mainly include the permanent magnetic beacon and the energized solenoid magnetic beacon. Based on BiotSavart′s theorem, a permanent magnet scheme with the tapered combination structure and the plane cross combination structure is proposed. The magnetic beacon design scheme is compared with the existing single permanent magnet beacon and the orthogonal permanent magnet beacon. The magnetic field signal performance of the conical combination structure magnetic beacon is best when the angle between two planes is 70°. And the directivity of the signal in the twodimensional plane can be significantly improved. The plane crosscombined beacon with a magnet angle of 60° is designed to address the issue of the existing signal beacons and increase the signal transmission distance. The magnetic field performance generated by the unidirectional combined beacon and the binary array combined beacon is analyzed for the plane crossing combined rotating magnetic beacon. Simulation results show that the magnetic field signal generated by array beacons is robust in the propagation media with different attributes. NdFeB permanent magnet material is used to build a signal detection and analysis system. When the baseline is 5 m and the beacon rotation angle difference is 0, the maximum errors produced by the two beacon layout methods are 355 nT and 580 nT, respectively. It proves that the proposed magnetic beacon design has practical value. The standard structure provides an effective solution to the problems of limited signal transmission distance and difficult signal extraction of the magnetic navigation positioning system.

Abstract:Abstract:Limiting cutting depth for evaluating the milling stability is dependent on the machining position. The consequence is that the stability constraint of the process parameters optimization model has uncertain. To solve this problem, the tool tip frequency response functions at different machining positions are combined with the milling stability theory. Firstly, a general regression neural network (GRNN) is formulated for predicting the positiondependent limiting cutting depth, which can be used to determine the milling stability constraint. Then, a process parameters optimization model of multipasses milling for minimizing cutting time is established. Displacements of the machine tool moving parts and cutting parameters for rough and finish milling processes are taken as variables. The particle swarm optimization algorithm (PSO) is utilized to solve this optimization model. A case study is implemented on a vertical machining center. The optimal combination of machining position and cutting parameters can be obtained, including the spindle speed, cutting depth, cutting width and feed rate per tooth. The total cutting time of the rough and finish processes decreases 2247% after the optimization. There is no chatter during the milling process, which verifies the feasibility of the proposed optimization model.

Abstract:Abstract:Aiming at the problem that the Gerschgorin disk estimator (GDE) and its improved algorithm cannot accurately calculate the number of changing instantaneous signal sources, an improved GDE method based on the combination of sliding window (SW) and correlation coefficient (CC) is proposed, which is called GDESWCC dynamic source number blind estimation method. Firstly, using the characteristic that the Gerschgorin disk radius of the GDE changes constantly in the increasing process of the number of signals, the dynamic segment with the largest radius change in the whole source is obtained by subtracting the old radius from the new Gerschgorin Disk radius. Secondly, the sliding window algorithm is used to precisely estimate the dynamic segment, and the judgment threshold of GDE for each sliding window is obtained. Then, the judgment threshold of GDE is taken as the characteristic quantity of the sliding window, and the correlation coefficients among them are calculated. According to the peak position of the correlation coefficients, the dynamic window signal disk and the static window signal disk are distinguished to obtain the number of instantaneous signal sources. Finally, computer simulation and actual experiment data verify the effectiveness, universality and practicability of the proposed algorithm. The comparison of computer simulation and experiment results shows that compared with the existing static GDE, the proposed algorithm can quickly interpret the number of signal sources and locate the dynamic changing time region. On this basis, the simulation experiment on dynamic source signal number estimation in the case of underdetermined blind source separation was carried out with the proposed algorithm combining ensemble empirical mode decomposition (EEMD). The results show that the correct estimation can be obtained as long as the adjustment factor is greater than 02. The actually measured data in experiment are basically consistent with the simulation results. Especially when the number of signal sources decreases, the estimated correct probability of GDE decreases from 95% to 4%, while the estimated correct probability for the proposed algorithm in this paper increases from 95% to 97%.

Abstract:Abstract:This paper aims at the winding vibration problem of transformer in unbalanced operation mode, studies the vibration principle of transformer in unbalanced operation mode. The winding electromagnetic vibration model under unbalanced operation mode is constructed, the wavelet packet transformation is used to decompose and reconstruct the vibration signal. Then, the energy distribution law at different frequency domain scales is studied. Furthermore, the vibration feature identification method based on the scaleenergy ratio is proposed. Dynamicmodel experiment was conducted to obtain the vibration information of the winding. The scaleenergy ratio eigenvalue is determined by the reconstructed signal of wavelet packet decomposition. The vibration characteristics of unbalanced operation winding are studied, which provides a new diagnostic means for transformer unbalanced abnormal operation condition. When the transformer is in normal operation, the double frequency component signal accounts for 4672% of the total signal energy. When the unbalanced operation α is -20% and 20%, the energy ratio of the double frequency decreases by 7567% and increases by 4207%, respectively. In unbalanced operation condition, the vibration characteristics of the threephase windings differ greatly. The generated unbalanced vibration moment will endanger the stability of the overall structure of the transformer windings.

Abstract:Abstract:The spindle is the core component of the numerical control machine tool. The vibration caused by the mass imbalance seriously affects the machining accuracy of the machine tool. To suppress the spindle unbalanced vibration, it needs to accurately extract feature of the vibration signal. To identify the unbalanced vibration amplitude and phase of the spindle system, a feature extraction method based on the allphase fast Fourier transform is proposed. The all phase fast Fourier transform can accurately extract the phase and amplitude of the signal by using spectrum analysis function. This method is compared with other three methods to extract the vibration feature of the signal collected by simulation and experiment. Results show that the allphase Fourier transform can achieve better vibration amplitude and phase accuracy and stability. The accuracy of the vibration phase after extraction can reach 97%, and the dynamic balance vibration suppression experiment can be reduced by 6521% after extraction. The effectiveness of the method is further verified.

Abstract:Abstract:Aiming at the problem that the accuracy of the waveform equation establishment in the sinusoidal error compensation process of the grating Moiré signal affects the error compensation effect, a waveform modeling method is proposed according to the requirement of actual subdivision number. On the basis of explaining the sinusoidal error compensation principle of grating Moiré signal based on PSO algorithm, the importance of the signal waveform equation establishment is expounded. Aiming at the problem of harmonic selection during the waveform equation establishment, the angle errors induced by the DC drift and harmonic contents are quantified, which provides a reference for the establishment of the waveform equation. Simulation experiments were used to verify the effectiveness of the model establishment. On the FPGA platform, the signal waveform parameter solution was achieved with the PSO algorithm and the influence of waveform equation on resource occupancy under different dimensions was compared. Finally, a grating system platform was established to verify the effectiveness of the proposed method. The results show that the proposed compensation method can effectively reduce the sinusoidal error component in the signal. The subdivision error is reduced from 074″ to 030″.

Abstract:Abstract:Baseline wander seriously influences the feature extraction and recognition of Electrocardiography (ECG) signals. The effect of the baseline correction method determines the accuracy level of medical diagnosis. A baseline correction algorithm for ECG signals based on empirical wavelet transform and piecewise polynomial fitting theory is proposed in this paper. Firstly, empirical wavelet transform is used to adaptively segment the spectrum of ECG signal, on the segmentation interval a suitable wavelet window is constructed to extract the empirical modal component with tight support. The empirical modal component with the baseline wander component removed is reconstructed. Then, the piecewise polynomial fitting is performed to remove the residual baseline wander from the ECG signal. The test results for the same ECG signal show that compared with the original empirical wavelet transform algorithm, the proposed algorithm improves the signaltonoise ratio (SNR) by more than 19 dB. The proposed algorithm can effectively correct the baseline wander distortion while maintaining good morphological characteristics of the ECG signal.

Abstract:Abstract:Visual image detection has great research significance and application value in the computer vision field. In recent years, the development of convolutional neural network (CNN) has led to the progress of visual image detection. A large number of new theories and new methods are applied to convolutional neural network, which improves the network feature expression ability, reduces the network complexity and improves the network performance. This paper presents the basic structure of Convolutional CNN, summarizes the improvements of CNN in recent years on different aspects, including convolutional layer, pooling layer, activation function, network regularization and network optimization, sorts various applications of CNN in visual image detection field and summarizes the advantages of CNN in visual image detection field, finally, prospects the future research direction.

Abstract:Abstract:In remote sensing images, ship objects have the characteristics of small size, slender shape, close arrangement of multiple objects and high similarity between classes. The existing deep learning object detection algorithms have low detection accuracy for small ship objects, and are prone to error detections and missed detections. In order to effectively utilize the remote sensing image information and improve the accuracy of small object detection, the SDNGV ship data set is constructed, and an improved single short multiBOX detector (SSD) ship object detection and recognition method based on concatenated rectified linear unit (CReLU) and feature pyramid networks (FPN) is proposed. Firstly, CReLU is added to the shallow layer of the SSD network to improve the transmission efficiency of its shallow layer features. Secondly, FPN is used to fuse the multiscale feature map used for detection in SSD step by step from the deep layer to the shallow layer of the network to improve the positioning accuracy and classification accuracy of the network. Experiments demonstrate that the proposed detection algorithm has good detection accuracy, the improved method has obvious effect, and the detection accuracy of small ship objects has 10 percent improvement.

Abstract:Abstract:A new method is proposed to achieve largescale cigarette filters detection under a wide field of view. It is expected to obtain accurate detection under the situation of overlapping, sheltering, distortion and low contrast. Firstly, by adding the spatially aware based the selfattention argument module and a Focal Loss function, an improved Unet model named as SAAUnet is proposed, which can achieve highly accurate semantic segmentation. After receiving a correct segmentation mask, the circle center of the cigarette filters is determined by object detection. Based on the circle theorem, the structural element matching is used to detect the circle centers. Hidden Markov model (HMM) is employed for direction searching. Experiments performed in simulation and industry application environments (with 5000 boxes) verify that the accuracy of the proposed approach can achieve 9995%. Results also show that the proposed method has strong robustness to adapt different challenging environments.

Abstract:Abstract:The outer surface of large remote sensing satellites consists of polyhedral structure. The overlap rate between images of adjacent surfaces are very low and the orientation target is visible in just a few images. As a result, it is difficult to orient the whole photogrammetric image network. The measurement accuracy of such structure is low. A photogrammetric network orientation method for large threedimensional (3D) structures is proposed. The relative exterior orientation parameters between images are solved by the 5point method using several coded targets. Then, a relative orientation link is built and extended to involve all the images by maximizing the number of coded targets. In this way, the backprojection error is minimized, and the global relative orientation of the photogrammetric image network of large 3D structures is realized. The method is applied to photogrammetric measurement of the out surface of a 35 m×35 m×22 m remote sensing satellite. Results show that the orientation completion and accuracy of the method are better than those of the traditional methods. The method provides accurate and reliable initial estimation of exterior parameters for the photogrammetric images of large spacecraft components with 3D structures. The standard deviation of exterior angle parameters is smaller than 0000 3 rad, and the standard deviation of translation parameters is smaller than 07 mm.

Abstract:Abstract:Aiming at the requirement of transfer service robots for high precision of human pose recognition and the problem of low recognition accuracy of existing human pose recognition methods under joint occlusion, this paper proposes a model constraintbased human pose recognition algorithm to solve the precise extraction of human body joint space coordinates of the robot system before the transfer operation. Firstly, the OpenPose algorithm is used to identify the pixel coordinates of the unoccluded joints in color image. Through aligning the color image and depth image of the RGBD camera, the joint pixel coordinates are converted into 3D coordinates. Then, the spatial coordinates of the occluded joint connected with the unoccluded joint are calculated according to the relevant parameters of the human model and the unoccluded joint coordinates, which are used to improve the recognition accuracy of the occluded joints. The experiment results show that the recognition accuracy of the proposed algorithm is 92% when the joint is unoccluded, and reaches 90% when the joint is occluded. The average time for a single frame calculation is about 190 ms, which meets the realtime requirements of transfer service robot operation.

Abstract:Abstract:Due to the concealment and randomness of the series fault arc, it is difficult to detect these faults accurately. The relatively small current amplitude is easy to be annihilated by the load current, and the load is highly correlated with the nature of the load. To solve these problems, a method based on the lowvoltage singlephase AC series fault arc experiment platform is proposed, which refers to the UL1699 standard. Two periodic currents of the electrical circuit are collected. The proportion coefficient of zero current time and the maximum correlation coefficient of the normalized absolute value after filtering the lowfrequency components are calculated. Then, two coefficients are fused by a fuzzy logic processor to obtain the comprehensive characteristic identification coefficient of the series fault arc. It is possible to identify whether there is occurrence of series fault arc by comparing the deep combination of the achieved coefficient and the proportion coefficient of zero current time with the empirical threshold value. Experimental results show that this method can recognize up to 100% of the series fault arc when the recommended load in GB142874 is used in the lowvoltage singlephase AC power circuit. There is no phenomenon of misjudgment and leakage.

Abstract:Abstract:Aiming at the problem of less research on stochastic resonance (SR) in twodimensional potential field, the SR mechanism and application of twodimensional tetrastable potential system (TTPS) under the combined action of Gaussian white noise and external weak driving force are discussed. According to the linear response theory, the probability flow method is used to calculate the spectral amplification factor (SAF) of the TTPS to external periodic driving frequency. The theoretical analysis result shows that when the SAF is used as an indicator, a significant SR phenomenon will occur in the TTPS. The SAF can be further improved by increasing the coupling coefficient and asymmetry coefficient or lowering the driving frequency. Then, combining the Chambers MalllowaStuck algorithm and fourthorder RungeKutta method, using the optimization parameters of genetic algorithm (GA), the TTPS is applied to weak periodic signal detection and bearing fault diagnosis, and compared with the new onedimensional tristable potential system (NOTPS). The experiment results prove the correctness of the theoretical analysis conclusion, and indicate that the TTPS can effectively detect the weak periodic signal and diagnose the faults of the inner and outer rings of the bearings. The amplitudes at the fault frequencies of the two bearing inner rings can be increased to 4195 and 2971 with TTPS, respectively, while those can only be increased to 2506 and 1034 with NOTPS, respectively. The amplitudes at the fault frequencies of the two bearing outer rings can be increased to 4087 and 3429 with TTPS, respectively, while those can only be increased to 2693 and 1866 with NOTPS, respectively, which proves that the performance of the TTPS is better than that of NOTPS.

Abstract:Abstract:Due to the refraction of the pipe wall, the flow images of gasliquid twophase flow in micro/minipipe obtained with visualization method are distorted, which fail to reflect the flow information accurately. The present paper investigates the image distortion under several common conditions through light path and simulation analysis. The results indicate that the images obtained with visualization method are linearly enlarged compared with the real structure, and the magnification ratio increases linearly with the increase of refractive index of the liquid phase. The influences of the inner diameter, wall thickness and refractive index of the pipe are relatively small. The magnification ratio is in the range between 130 and 144 under normal circumstances. Practical void fraction measurement experiments in millimeterscale pipes were carried out. With the image distortion correction method, the maximum absolute error of the measurement results is decreased significantly (the decrements for the pipes with inner diameters of 206, 414 and 422 mm are 546%, 466% and 516%, respectively), which verifies the effectiveness of this correction method. This study reveals the important influence factors of image distortion, which is of great important significance for accurately obtaining the flow information of gasliquid twophase flow in micro/minipipes.

Abstract:Abstract:The coded ultrasonic testing has the problem of long duration of mainlobe and high level of sidelobe after pulse compression. To solve this issue, a composite coded ultrasonic excitation technique based on the nonlinear frequency modulation Barker is proposed, which is applied in the plate weld flaw detection. The generation method of the proposed composite code is presented and its mathematical model is deduced. The timefrequency performance, the transducer response, and the pulse compression performance have been researched and simulated, which are compared with other coded signal excitation method, such as Barker, nonlinear frequency modulation signal, and linear frequency modulation Barker. Compared with the nonlinear frequency modulation signal and linear frequency modulation Barker, experimental results show that the power ratio of mainlobe to sidelobe is increased about 11 and 5 dB, respectively. Compared with Barker code, nonlinear frequency modulation signal, and linear frequency modulation Barker code, the level of peak sidelobe is reduced about 78, 7, and 36 dB, respectively. The composite coded ultrasonic signal can effectively detect the common five types of defects on a steel plate weld sample.

Abstract:Abstract:It is difficult to detect and identify small defects on the surface and subsurface of wire arc additive manufacturing (WAAM) formed parts. To solve this problem, the texture feature of images and neural networks are both utilized. A nondestructive detection method based on magnetooptical imaging is proposed to detect surface defects of low carbon steel WAAM formed parts detection and classification. Firstly, WAAM formed parts are magnetized after processing by surface finishing. Magnetooptical images of the surface of formed parts are obtained by the magnetooptical imager as test samples. Then, the texture feature of angular second moment, entropy, contrast and correlation of magnetooptical images are extracted by the graylevel cooccurrence matrix after preprocessing the images and texture feature data of four different surface qualities. To be specific, perfectness, poor fusion, depression and cracks are used to carry out comparison. Finally, the classification of formed parts is predicted by LevebergMarquard （LMBP） neural network. Experimental prediction results show that the surface defect detection rate of low carbon steel WAAM formed parts is 9733% and the classification accuracy rate of the surface quality can reach 9133%. These results verify that the proposed method can effectively detect and identify small surface defects on surface of low carbon steel WAAM formed parts.