Qr Code
-
WeChat
- Current Discuss
- First Published
- Album Paper
-
Recent progress on soft haptic feedback actuators
Yu Meng, Cheng Xiang, Peng Shigang, Wang Pengfei, Zhao Liangyu
Abstract:
As an important communication window between the user and the system, the haptic interface not only meets the user′s need for a simple and natural human-machine interface (HMI), but is also a key factor in achieving tasks in a direct and efficient way. As the core of the haptic interactive process, the haptic feedback actuators are capable of simulating or reproducing diverse forms of haptic information to improve the immersion and realism of the user during the operation process. In recent years, soft haptic feedback actuators have gradually become a research hotspot for HMI due to their light-weight, flexible, and adaptable characteristics, offering extensive potential and value in various human-machine interaction scenarios. First, according to the different stimulation forms to the skin, soft haptic feedback actuators can be divided into three categories, electrical stimulation, mechanical stimulation, and thermal stimulation. Based on this, the recent progress of various soft haptic feedback actuators is introduced and summarized, including the actuation principle, design, and output of haptic feedback actuators. Further, the applications of haptic feedback devices developed based on soft haptic feedback actuators in the fields of virtual reality, teleoperation, and medical implications are also presented. Finally, the current problems and challenges of soft haptic feedback actuators are summarized, and the future development trend is discussed.
-
Butterfly-shaped FeGa film / AT-cut quartz wafer double-sided composite resonant magnetic field sensor
Yang Xiaopeng, Wen Yumei, Chen Dongyu, Li Ping
Abstract:
Due to magnetic convergence, the magnetostrictive strain in the central region of a butterfly-shaped magnetostrictive film is enhanced. However, when the butterfly-shaped magnetostrictive films are combined with the rectangular AT-cut quartz wafer, the strain in the electrode region of the rectangular wafer spreads out to the surrounding non-magnetic film region and thus reduces the effect of the strain on the thickness-shear vibration of the wafer and consequently reduces the sensitivity of magnetic field sensor. In this paper, a butterfly-shaped resonant magnetic field sensor with the same shape of AT-cut quartz wafer as the FeGa films sandwiching the wafer is proposed. The butterfly-shaped sandwiched composite can concentrate the strain enhanced by the butterfly-shaped film into the electrode region of the wafer to improve the sensor sensitivity. The simulation results predict that the sensitivity of the butterfly-shaped structure is 3. 73 times that of the butterfly-shaped magnetic film/ rectangular wafer structure. By MEMS fabrication, we developed the butterflyshaped sensor, and the characterization of the actual device shows that the sensitivity can reach -29. 08 Hz/ Oe in the linear range of 76. 4~ 117 Oe .
-
High performance multi-directional dual circular piezoelectric energy harvester
Abstract:
To solve the problems associated with the narrow bandwidth and low power density in conventional piezoelectric energy harvesters, a piezoelectric energy harvester with dual circular cantilever beams spliced inside and outside the inverse direction is designed with the linear multimodal resonance method. Each of the two beams is covered with a piezoelectric layer and the piezoelectric layers are connected in series. A two-degree-of-freedom system lumped parameter model and an electromechanical coupling model are studied to analyze the frequency response and output performance. The performance of the piezoelectric energy harvester is assessed with different radians of the piezoelectric layers, achieving optimal output performance when the radian is 0. 5π. Setting the excitation acceleration at 0. 1 g, the resonant frequency is 82. 19 Hz, the open-circuit voltage is 49. 65 V, and the maximum output power is 3. 74 mW in the first-order mode. The resonant frequency is 119. 14 Hz, the open-circuit voltage is 44. 74 V, and the maximum output power is 3. 54 mW in the second-order mode. The measured results show that the structure can effectively broaden the bandwidth to 52 Hz with high power density. This piezoelectric energy harvesting system is simple in structure, easy to manufacture, wide in a working frequency band, and can supply power in the environment with large frequency fluctuation. Meanwhile, it has the advantages of small volume, high power density, and high-efficiency energy collection under multi-directional excitation. Therefore, this system can be applied to many fields, such as wearable devices, low-power devices, and so on.
-
Method of levitation gap detection for maglev train based on electromagnet with a composite coil
Jing Yongzhi, Liu Qinyu, Jia Xingke, Ni Sheng, Yang Liangtao
Abstract:
To address the problems of large volume, inconvenient to directly measure the real electromagnetic gap of traditional eddy current sensors, a method of levitation gap detection for maglev train based on electromagnet with a composite coil is proposed. Firstly, the equivalent electromagnetic gap detection principle for the levitation system is analyzed, and the equivalent impedance model of the composite coil is formulated for the coupling effect of electromagnet and high-frequency eddy currents in the surface of the F-rail on the magnetic field of the composite coil. Secondly, the excitation frequency and the structure of the levitation system based on the gap detection of the composite coil are optimized through finite element simulation to improve the detection sensitivity. The resonant detection circuit and signal demodulation circuit are designed to effectively suppress the crosstalk between the levitation system and the gap detection system. Finally, the static performance test and levitation experiment are carried out, the linearity is 0. 4% after correction, the maximum fluctuation peak-to-peak value of the static output is 89μm at constant gaps, the suspension system is capable of stabilizing the suspension under different operating conditions for a given gap, step disturbance and offset disturbance. The experimental results show that the response speed and detection error of gap detection meet the system requirements.
-
Review on progress of data-driven based health state estimation for lithium-ion batteries
Abstract:
Lithium-ion batteries (LIBs) are widely used in areas such as electrified transportation, electrochemical energy storage and mobile electronics. Consequently, accurate assessment of their state of health ( SOH) is fundamental to ensure safe and reliable applications. Data-driven methods are the mainstream methods to evaluate SOH, which do not need to consider the complex physical and chemical reactions inside the battery, and only rely on direct data analysis to achieve accurate SOH estimation. This paper analyzes the current research progress of data-driven estimation methods for battery SOH under the consideration of the influencing factors of SOH for LIBs, and focuses on comparing the principles, advantages and disadvantages of machine learning, filter and time series methods in implementing SOH estimation. Finally, according to the practical application scenarios of electric vehicles, the future development trend of SOH estimation methods is prospected.
-
A fault diagnosis method for harmonic reducers under different operating conditions based on information fusion subdomain adaptation
Kang Shouqiang, Zhang Weidong, Wang Yujing, Liu Liansheng, Sun Yulin
Abstract:
In response to the significant variations in data distribution of industrial robot harmonic reducers under different operating conditions, the partial absence of data labels for certain conditions, and the incomplete information obtained from a single sensor, which together result in low diagnostic accuracy, a fault diagnosis method is proposed based on information fusion and subdomain adaptation for different operating conditions of harmonic reducers. Time-frequency graphs are constructed using wavelet transform on one-dimensional vibration data from source and target domains. Time-frequency information from multiple sensors is integrated using a wavelet transformbased image fusion method, and the fused image is created. To fully exploit the multi-representational features of the fused samples, an improved residual network with a multi-representation feature extraction structure is proposed. Simultaneously, in an unsupervised scenario, the multi-representation features of the fused samples from the source and target domains are subjected to subdomain adaptation, for reducing the distribution differences between subdomains of both domains. Transfer the knowledge from the label-rich source domain to the label-deficient target domain, and ultimately fault diagnosis of harmonic reducers can be achieved under different operating conditions. By establishing an experimental platform for the industrial robot harmonic reducers and conducting actual measurements, the proposed method can achieve an average accuracy of 98. 8% for all transfer tasks, and effectively enable fault diagnosis of harmonic reducers under different operating conditions in an unsupervised scenario.
-
Research on sensor self-diagnosis design method based on redundancy relationship analysis
Jiang Dongnian, Chu Tianrui, Gao Yuxin
Abstract:
In industrial processes, the multitude of sensors requires high reliability, yet traditional routine inspection methods for assessing their health status are not only time-consuming and labor-intensive but also fail to meet the demands for sensor intelligence development. To address this issue, a sensor self-diagnostic design method based on the statistical correlation of measurement data is proposed. This method establishes statistical relationship models using sensor measurement data and utilizes auto-encoders to extract features from sensor data and encode them in binary form. Considering both statistically independent and correlated situations of sensor measurement data, a statistical model for independent diagnosis is established by introducing fault detection probability and false alarm probability when reference values are available. In the absence of reference values, a multivariate statistical dependency model using the Gaussian Copula function is constructed to assess the correlation among parameters. Furthermore, relying on Bayesian theory, the model autonomously learns to ascertain the health status of sensors without reference values. The proposed method is demonstrated using a nickel flash furnace system as an example. In both modes, the posterior probability of sensor fault detection reaches 0. 92, indicating that the parameters of the fault statistical model align with the modeling expectations. Experimental results confirm that the proposed method accurately identifies faulty sensors in the measurement system under both modes, thereby validating its effectiveness and feasibility.
-
Prediction of remaining life of motor bearings using multi-sensor fusion and MHA-LSTM
Zhang Wan, Zhang Taiyu, Jia Minping, Cai Jun
Abstract:
As a core component of motors, bearings primarily serve functions such as supporting and guiding shafts, reducing friction in equipment, and connecting different components. Predicting the remaining life of bearings is crucial for system health management. However, single sensor signals often fail to comprehensively describe the potential degradation mechanisms of the system. This paper proposes a novel approach for predicting the remaining life of motor bearings based on the multi-head attention mechanism and long shortterm memory neural network. Firstly, Mahalanobis distance is used to determine the starting point of bearing performance degradation by dividing the entire life cycle of rolling bearings into normal and degradation phases. Secondly, an Autoencoder is employed to automatically extract vibration signal features, which are subsequently fused with motor current and bearing temperature signal to construct a multi-source information feature matrix. Subsequently, the multi-head attention mechanism and long short-term memory network dynamically select features with high relevance, thereby improving the accuracy of the remaining life prediction. Finally, the model is validated using experimental data, and the results show that the proposed model has higher accuracy.
-
Hierarchical decomposition of CNN for resource-constrained mechanical vibration WSN edge computing
Fu Hao, Deng Lei, Tang Baoping, Li Zihao, Wu Yanling
Abstract:
The microcontroller of wireless sensor network (WSN) nodes used for mechanical vibration monitoring requires intricate edge computing, yet face limitations in hardware resources. Convolutional neural network (CNN), as a high-performance and commonly used deep learning algorithm, can enhance the computational capabilities of edge WSN nodes when run on microcontroller units (MCUs). This paper proposes a hierarchical decomposition method for CNN models without modification, addressing the challenge of running nonlightweight CNN on resource-constrained MCU and enhancing the computational capabilities of mechanical vibration WSN nodes. First, a file structure is designed to decompose and store CNN model parameters. Subsequently, a memory management method is proposed, and the consumption process of random-access memory is derived. Finally, a parameter localization method is introduced to accurately and efficiently retrieve model parameters. Experiments demonstrated that with only 1. 76 KB of RAM and 2. 14 KB of Flash, high-precision edge computing recognition tasks can be accomplished within 3. 15 ms.
-
Remaining mechanical useful life prediction for circuit breaker based on convolutional variational autoencoder and multi-head self-attention
Sun Shuguang, Wang Zewei, Chen Jing, Huang Guanglin, Wang Jingqin
Abstract:
Arming at the uncertainty of the degradation of conventional circuit breakers and the perfect mechanical degradation characterization by vibration signals, an opening mechanical mechanism life prediction method based on CVAE and MSA mechanism is proposed. Firstly, the parametric features are extracted based on the different event intervals of the circuit breaker. Then, the depth features in the signal components are mined by CVAE, and the parametric features are fused with the depth features to obtain the complete degradation features. Finally, the quantitative life prediction model of the GRU-MSA is formulated, which introduces MSA to capture the different dependencies of signals in several different representation subspaces and assign greater weights to the important time steps. Finally, the proposed method is tested by using the vibration signal measurement data of three test samples. The results show that the proposed method has life prediction RMSE of 141. 46, 128. 75, and 134. 16, and MAE of 112. 17, 101. 52, and 106. 22, respectively. The prediction accuracy is high and the stability is good, which has more advantages compared with other hybrid prediction models.
-
Remaining life prediction of different types of rolling bearings based on subspace domain adversarial discrimination network
Chen Renxiang, Zhang Yanfeng, Xu Xiangyang, Zhang Pengbo, Yang Baojun
Abstract:
A residual life prediction method for different types of rolling bearings is proposed based on the subspace domain adversarial discriminant network ( SDADN) to address the issue of inconsistent distribution and characteristic scales of bearing degradation data caused by differences in structural dimensions, operating conditions, and other factors, leading to a decrease in life prediction accuracy. Firstly, the feature extractor can adaptively obtain deep degradation features for different types of rolling bearings by using an efficient channel attention mechanism to enhance the weight of important features in each channel and is used to train the label predictor. Then, in the asymmetric feature mapping framework, the domain discriminator and feature extractor are adversarially trained to minimize the orthogonal basis distance between the source and target domains in the representation subspace. By utilizing the property that the orthogonal basis in the representation subspace is not affected by feature scaling, the regression performance degradation caused by excessive feature scale changes is reduced, and domain adaptation among different types of rolling bearings is achieved. Finally, the trained feature extractor is used to extract the degradation features of the bearing, and the remaining lifespan is obtained by inputting them into the label predictor. The proposed method was validated on PRONOSTIA, XJTU-SY, and self-test datasets, and the experimental results showed that it can fully learn the distribution information of source domain features, effectively overcome the feature scale differences under different models, and improve the performance by 20% to 40% compared to other domain adaptive methods.
-
Research on fault diagnosis of wind turbine icing characteristics based on LeNet5like transfer learning
Lyu You, Feng Shuo, Zheng Xi, Deng Dan, Liu Jizhen
Abstract:
A fault diagnosis of wind turbine icing characteristics based on LeNet5like transfer learning method is proposed, to address the problems of low accuracy and slow modelling speed of icing characteristics fault models, which wind turbine units are in offshore wind farms and high altitude areas. Firstly, the recorded data from the SCADA system and the wind turbine icing situation are pre-processed to build a training dataset; secondly, the icing fault diagnosis model is constructed based on the improved LeNet5like network to extract the correlation feature information between multiple variables in the dataset; then, the model is trained by the transfer learning finetuning to achieve the rapid establishment of ice-cover fault diagnosis models for other wind turbines; finally, the model is experimentally validated to have an icing fault diagnosis accuracy of 98. 90% , a 28 s reduction in training time and an improvement of about 15. 91% over the transfer module-free network, verifying the accuracy and speed of the LeNet5like based transfer learning wind turbine blade icecover fault diagnosis method.
-
The application of high-frequency / high-field electron paramagnetic resonance in the research on spin qudits
Yuan Jiayue, Fu Pengxiang, Gao Song, Jiang Shangda, Zhou Shen
Abstract:
Electron spin resonance (EPR) is a powerful technique to study the electron structure and dynamics of many materials with unpaired electrons ( paramagnetic materials). Nowadays, EPR has been widely applied in chemistry, material, radiation detection, biology, and quantum information processing. Compared with the X-Band EPR, the high frequency / field EPR ( HF-EPR) has the advantages of resolution, sensitivity, and initialization. This article is a concise introduction to the history, basic theory, instrumentation, and characteristics of the HF-EPR, and the emphasis on the application of HF-EPR in the research on spin qudits. The progress and future prospects of this analysis technique are also outlined. The HF-EPR could be used as a tool to implement the coherent manipulation of spin qudits, further paving the way toward quantum logic gate operation and quantum algorithm.
-
Multi-layer nonlinear local receptive field extreme learning machine method for logging gas analysis
Li Zhongbing, Yuan Zhangyu, Liang Haibo, Chen Guihui, Jiang Chuandong
Abstract:
With China′s increasing energy demand and the complex drilling environment, it is of great significance to carry out highprecision detection of alkane gas concentration to improve oil and gas exploration efficiency. Spectral logging technology has become a research hotspot in the process of oil exploration with the advantages of quick and accurate recording results. In this article, a multi-layer nonlinear local receptive field extreme learning machine (NM-LRF-ELM) model is proposed for resolving nonlinear problems caused by saturation absorption, noise interference, and baseline drift. The model converts one-dimensional spectral data into two-dimensional matrix format and realizes nonlinear feature extraction between input and hidden layer by using local receptive field data processing. Meanwhile, an improved T-sigmoid activation function is introduced and the dropout layer is added after the fully connected layer to reduce the overfitting risk of the model. The feature extraction and quantitative analysis of the model show an integrated structure and directly outputs the predicted value of quantitative analysis. In this article, the infrared spectra of 407 mixed alkane gas samples from two groups are collected as an experimental data set for quantitative analysis. The experimental results show that the training time of this model is reduced by more than 90% compared with the sliding window model and the gray Wolf model, and the prediction accuracy of the model is still lower than the system error under the nonlinear interference of the homolog. Therefore, the proposed method is helpful in reducing the nonlinear interference of unknown gas and improve the infrared spectrum detection accuracy of target gas under the condition of complex field environment changes.
-
Ultrasonic phased array experimental device for liquid film thickness measurement
Zhao Ning, Sun Mingcong, Liu Miaomiao, Pang Lili, Zhang Rongxiang
Abstract:
Gas-liquid two-phase flow exists in processes such as nuclear reactor evaporation, aircraft cooling, and chemical production falling-film evaporation. Dynamic measurement of interfacial waves is of great significance for industrial process monitoring and production optimization. The accurate identification and characteristic parameter measurement of interfacial waves are important prerequisite for scientific research and engineering practice. Based on the ultrasonic phased array measurement system, the measurement method of sector scan is designed, which can be used for liquid film thickness and three-dimensional measurement of interfacial wave morphology with clear gas-liquid interfacial. Through static calibration and circular tube verification, the relationship between pixel points and liquid film thickness is determined. The real-time dynamic experiments are carried out under the gas superficial velocity is 0. 071 9~ 0. 431 6 m/ s and the liquid superficial velocity is 0. 056 7~ 1. 416 1 m/ s. The cross-sectional gas-liquid interfacial information with high accuracy in real-time flow process is obtained, and the three-dimensional distribution of interfacial waves is built, which provides an experimental reference method for the study of interfacial wave characteristics parameters K .
-
A multi-criteria active learning method based on adaptive density clustering
He Zhonghai, Zhu Wenhan, Chen Xuwang, Zhang Xiaofang
Abstract:
Active learning proves instrumental in training superior machine learning models while minimizing labeling costs. The combination of RD and QBC algorithms effectively addresses issues associated with considering only a single criterion. However, the K-means clustering upon which RD is based may include outliers, leading to a decrease in model performance, and QBC requires maintaining multiple models and indirectly provides sample information. To address these issues, we propose an adaptive density clustering-based Gaussian process regression ( ADC-GPR) algorithm, which efficiently selects samples by first clustering and then utilizing uncertainty directly. The ADC clustering in this algorithm is not only robust against outliers but also adapts to the distribution characteristics of the dataset, providing representative sample points and their corresponding clusters for subsequent AL. This method ensures both representativeness and diversity in unsupervised selection and considers informativeness, representativeness, and diversity in supervised selection. The experimental results demonstrate that compared to the RS, KS, and RD-GPR algorithms, the ADC-GPR algorithm exhibits an average performance improvement of 37. 3% , 8% , and 2. 8% respectively, with the same number of sampling iterations. Furthermore, the ADC-GPR algorithm demonstrates higher selection efficiency.
-
Research on the method for simultaneously detecting piston and tip-tilt errors of segmented telescopes based on multiple CNNs
Abstract:
Most large telescopes adopt the design scheme of segmented mirror. In order to obtain high-quality imaging effect, it is necessary to control the piston and tip-tilt errors of segmented telescope system. Compared with traditional detection methods, the error detection method based on neural networks has some advantages, but it is limited to detecting only a single type of error. This paper proposes a method for synchronous detection of piston and tip-tilt errors based on a multi-convolutional neural network. By setting a mask with a sparse sub-pupils configuration at the exit pupil, the sub-waves reflected by the segmented mirrors generate interference-diffraction phenomena, thereby constructing a dataset containing rich piston and tip-tilt errors information. The design includes coarse measurement and fine measurement networks to meet the requirements of large-range and high-precision synchronous detection. Results demonstrate that the method achieves nanometer-level detection of piston errors within the coherent length of the input light source and submilliarcsecond detection of tip-tilt errors within a range of 10 μrad. The method exhibits robust resistance to 40 dB CCD noise, a tolerance of 0. 05 λ RMS (λ0 = 600 nm) for surface shape errors, and portability to six-mirror systems. Additionally, the method has simple optical path, convenient operation and practical significance.
-
A complementary Gray code double N-step phase-shifting method for eliminating periodic spurious tones phase errors
Han Shuhuan, Yang Yanxi, Zhang Xinyu, Li Xinjie
Abstract:
The complementary Gray code double N-step phase-shifting method has become a research hotspot in the field of fringe projection profilometry because of its good robustness and high detection accuracy. However, the traditional complementary Gray code double N-step phase-shifting method has the problems of low detection efficiency and does not eliminate the periodic spurious tones phase error. To solve these problems, the traditional complementary Gray code double N-step phase-shifting method is improved to eliminate periodic spurious tones phase errors in this paper. Firstly, the deformed fringe images are captured by camera to calculate two groups of wrapped phases. Next, the correlation of the two groups of wrapped phases is used to eliminate the phase difference and periodic spurious tones phase error in the detection data, and the two groups of wrapped phases are fused. Finally, the fused wrapped phase is unwrapped by the complementary Gray code phase unwrapping method. The experimental results show that the method in this paper could effectively eliminate the periodic spurious tones phase error and obtain the high-precision unwrapped phase, compared to the complementary Gray code double N-step phase-shifting method that does not eliminate periodic spurious tones phase errors, the accuracy of the detection method in this paper has been improved by about 24. 57% . Compared with the complementary Gray code N-step phase-shifting method, the accuracy of the detection method in this paper has been improved by about 6. 29% . Keywords:double N-step phase shift method; wrapped phase; periodic spurious tones ph
-
A study of the effect of waterborne pressure on ultrasonic guided wave signals in pipelines
Hu Xiaodie, Lin Tingwei, Zhang Weixuan, Wei Ziqi, Wang Yishou
Abstract:
In order to study the effect of water-loaded pressure on the ultrasonic guided wave signals, the dispersion curve and wave structure of guided wave in the pipe are analyzed by using the semi-analytical finite element (SAFE) method. The L(0,2) mode guided wave is selected in the detection experiment. A waterproof clamp that can withstand a pressure of 30 MPa is designed to encapsulate the piezoelectric transducer on the pipe. The encapsulated pipe is placed into a pressure chamber for the pressure cycle test, where the pressure varies between 0 MPa and 30 MPa. The guided wave signals of the heathy pipe and the damaged pipe with 5% area loss are collected during the pressure cycle experiments in order to analyze the effect of the water-loaded pressure on the guided wave signals. The experimental results show that the waterproof clamp has good waterproof and pressure-resistant properties, and at the same time, the water-loaded pressure does not have a significant effect on the amplitude of the L(0,2) modal waveguide signal.
-
Fast preliminary sorting strategy for retired batteries based on pulse voltage frequency domain features and internal resistance
Wang Yuhang, Huang Haihong, Wang Haixin
Abstract:
With the development of the new energy industry, how to deal with more and more retired batteries has become an urgent problem. Lithium iron phosphate batteries are widely used in automotive and energy storage scenarios due to the advantages of high energy density and safety. It is one of the mainstreams of existing retired batteries. The secondary utilization scenario of retired LiFePO4 batteries is evaluated based on the health status of the battery, internal resistance, and other states. But, this process consumes a lot of time. In this article, we propose to use the frequency domain characteristics of the voltage during the pulse process as the health features for estimating the health state. Then, the random forest regression algorithm is used to achieve a fast estimation of the health state, which greatly shortens the time for the sorting of decommissioned batteries. On this basis, this article proposes the use of an abnormal parameter identification method based on Gaussian distribution to evaluate the abnormal internal resistance of retired lithium iron phosphate batteries. Through experimental evaluation, the maximum error of health state estimation in the selected 15 LiFePO4 batteries is 6% , and the proposed method can effectively screen out the retired LiFePO4 batteries whose internal resistance does not match with SOH.
-
Adaptive denoising for leak-induced acoustic in gas pipe under multiple conditions
Xue Sheng, Xie Xiaoxian, Zheng Xiaoliang, Wang Qiang
Abstract:
To achieve denoising of pipe leak acoustic signal under the conditions of extremely low signal-to-noise ratio, based on the signal correlation among multiple channels, a correlation coefficient matrix is presented to determine the modes obtained by using the variational mode decomposition. For the leak signals under different conditions, the quality evaluation index for denoising that does not rely on the real value is presented to use it as the object function of the multi-objective grey wolf optimization algorithm. The best mode number K and penalty factor η of the variational mode decomposition are determined according to the Pareto front, achieving adaptive denoising under multiple conditions. An experimental rig of gas pipe leak under multiple conditions is established to evaluate the effectiveness of the proposed method under multiple conditions and with different signal to noise ratios (-8~ 4 dB) of input signals. The results show that this method can effectively suppress noises. Even in the case of -8 dB, the signal-to-noise ratio of denoised signals is amplified by more than 2. 84 dB. Compared with the method based on single-objective optimization, at -8 dB, the signal-to-noise ratio and correlation coefficient of the new method are increased by 3. 65 dB and 31. 26% , respectively.
-
Modeling and application of coupled modulation of vibration signals in wind power gearboxes
Wu Yingjie, Tong Yuan, Li Pengfei, Tian Ye, Wang Jianguo
Abstract:
The coupling modulation phenomenon between different gear trains of wind turbine gearbox interferes with the actual fault diagnosis. Therefore, a phenomenological model considering interstage coupling modulation is proposed for a two-stage planetary onestage parallel gearbox. Firstly, based on the amplitude and frequency demodulation analysis of vibration signal of wind power gearbox, the characteristics of series modulation between stages under multistage transmission are defined, and the series modulation model between amplitude-frequency stages is proposed. By constructing multistage amplitude-modulation signal and frequency-modulation signal, the series modulation characteristics are simulated in both the spectrum and demodulation spectrum, and the sideband energy index of coupling modulation characteristics is proposed for model evaluation. A scaled gearbox test bench with the same structure as the field gearbox is designed, and experiments and field analysis are carried out under both normal and fault conditions to verify the validity of the series modulation model between stages. The results show that the frequency coupling modulation phenomenon is particularly obvious in the four states, and the sideband energies of the coupling modulation characteristics are 1. 02, 1. 04, 1. 18 and 1. 25, respectively. The coupling modulation between gearbox stages reflected in this model is the gearbox itself, and will not change with the gearbox state. This model provides a reference for improving the fault diagnosis accuracy of wind power gearbox.
-
A main bearing fault feature enhancement method based on cyclical information extraction
Luan Xiaochi, Zhao Junhao, Sha Yundong, Tong Xinyu, Zhang Zhenpeng
Abstract:
In response to the problem of insufficient feature information extraction when the main bearing of aircraft engine fails, a method for enhancing the fault characteristics of main bearings based on cyclic extraction of effective information is proposed. Firstly, the original vibration signals are decomposed using wavelet packet decomposition, and the correlation coefficient and kurtosis values of each node component are calculated and normalized, and then fused into a comprehensive parameter Pi. Secondly, a confidence interval is defined based on the feature information cyclic extraction criterion, which divides all node components into three parts: high signal-tonoise ratio signals, low signal-to-noise ratio signals, and high noise signals. Then, high signal-to-noise ratio signals are continuously selected until the termination condition is reached. Finally, all high signal-to-noise ratio signals are reconstructed, and envelope demodulation is performed to extract the weak fault characteristics of the bearings. Simulation signal verification shows that the signal-tonoise ratio of the denoised signal is improved by 11. 31 dB compared to before denoising. The effectiveness of the feature information cyclic extraction method is comprehensively verified based on the data measured from a simulated test bench for intermediate shaft bearings in aircraft engines, and an analysis of the vibration signals of a certain type of aircraft engine main bearings is conducted. Practice shows that This method is suitable for feature extraction of rolling bearing under the condition of strong background noise interference, and can accurately diagnose the main bearing fault of aircraft engine.
-
Goal allocation and cooperative path finding for multiple UGVs
Gu Yitian, Zhang Tao, Zhang Liang, Yang Taihong
Abstract:
Aiming at the shortcomings of MAPF in the scenarios of anonymous cooperative path finding for multiple carlike robots, including long path and poor efficiency, a cooperative goal allocation and path finding algorithm for multiple unmanned ground vehicles, Nutcracker-CBS, is proposed. Firstly, a tightly coupled MAPF framework with goal allocation is constructed to optimize goal allocation and path finding jointly. In goal allocation module, an improved nutcracker optimization algorithm is proposed to solve goal allocation incrementally, which can shorten the duration of the module. In path finding module, an improved MAPF algorithm is proposed with traceback constraint construction mechanism, bypass mechanism for the length estimation of collision-free path, and low-level path planning mechanism for data sharing, to enhance efficiency and quality of path finding. In benchmark experiments, time cost of Nutcracker-CBS is reduced by 90. 37% compared to SOTA algorithm. Time consumption of goal allocation module is reduced by 86. 76% compared to the original algorithm. MAPF module completes the path construction of 100 unmanned ground vehicles within 6 seconds, with the reduction of average path length by 6. 058% . Filed test shows that the total path length is reduced by 55. 26% and the total time consumption of the system is reduced by 61. 29% , which boost the efficiency of multi-robot system and decrease path length.
-
Time delay compensation-based parallel active disturbance rejection control for permanent magnet synchronous motors
Yin Shixun, Zheng Zhian, Zhu Junjie
Abstract:
To address the issues of speed susceptibility to internal and external disturbances under various operating conditions in permanent magnet synchronous motor (PMSM), a parallel linear active disturbance rejection control (PLADRC) strategy based on delay compensation is proposed. Aiming at the problem that PMSM may be subject to the external time lag effect introduced by signal processing, inverter response, and other factors, the Smith predictor is introduced in combination with the active disturbance rejection control (ADRC) to make the control system respond to the internal parameter changes and external perturbations more accurately and quickly. Meanwhile, for the problem of poor anti-disturbance performance of linear ADRC (LADRC) in limited bandwidth, a parallel LADRC is designed to effectively improve its anti-disturbance capability while keeping its bandwidth unchanged and its parameters easy to adjust. Finally, the stability of the LADRC is analyzed, and the parameter design and perturbation performance are analyzed on this basis. Simulation and experimental results show that the proposed algorithm improves the adjustment time by 52. 5% , 49. 5% , and 42. 4% compared with LADRC after the motor is subjected to speed step, load perturbation, and internal parameter change, which verifies that the control strategy effectively enhances the resistance to internal and external perturbation and speed tracking ability of the PM synchronous motor under multiple operating conditions.
-
Research progress of vision-based rust defect detection methods for metal fittings in transmission lines
Liu Chuanyang, Wu Yiquan, Liu Jingjing
Abstract:
As a common defect type, surface rust of metal fittings in transmission lines is one of the important hidden dangers endangering the safe operation of transmission lines. How to quickly and accurately discover and repair rusted metal fittings is an urgent problem to be solved in the work of transmission line inspection. This article reviews the research progress of vision-based rust defect detection methods for metal fittings in the last ten years. Firstly, the rust defect detection process of metal fittings based on traditional image processing is introduced. Then, the rust defect detection of metal fittings is summarized according to traditional image processing and deep learning methods. The application of object detection and semantic segmentation algorithms in rust defect detection of metal fittings is emphasized. Next, the self-built data sets for metal fittings′ rust defect detection and performance evaluation indexes are introduced. Finally, the existing problems of rust defect detection methods based on deep learning are pointed out and future research work is prospected.
-
Precision improvement method of star centroid positioning based on multi-image super-resolution reconstruction for fine guide sensor
Wang Wenrui, Zhang Quan, Gao Yuanpeng, Fang Chenyan, Yin Dayi
Abstract:
The accuracy of the fine guide sensor′s star centroid positioning determines the accuracy of the visual axis attitude calculation of the space telescope. To improve the positioning accuracy of the star centroid of the fine guide sensor, a star image super-resolution reconstruction method based on the deep wavelet recurrent neural network is proposed. Firstly, the micro-scanning technology is used to obtain the sub-pixel misalignment low-resolution star image sequence, and the wavelet domain features of the low-resolution star image are extracted by using the wavelet encoder while the noise of the low-resolution star image is constrained by the wavelet coefficients. The registration process of the input star image sequence is integrated into the network learning. Secondly, the convolutional gate recurrent neural unit is used to fuse the features of the extracted star image sequence. Finally, the inverse wavelet decoder is utilized to decode the wavelet domain features output by the multi-feature fusion module. In this way, the de-noising and super-resolution reconstruction based on low-resolution star image sequences are realized. The experimental results show that the square-weighted centroid method is used to obtain the centroid positions of each star point in the original star image and the reconstructed star image with super-resolution. Compared with the former, the average centroid positioning accuracy and stability of each star point in the X direction are improved by 64. 76% and 19. 15% , respectively. In the Y direction, the accuracy and stability are improved by 75. 35% and 26. 14% , respectively
-
Spherical regularized support vector description for visual anomaly detection
Deng Shizhuo, Teng Da, Li Xiaohong, Chen Jiaqi, Chen Dongyue
Abstract:
Anomaly detection is an important task in the computer vision, such as medical, security. One of the challenges in anomaly detection is not easy to obtain large-scale annotated anomalous data. Existing methods focus on one-class classification and weakly supervised learning. Deep support vector data Description (Deep SVDD) is an important method to realize one-class anomaly detection. However, previous Deep SVDD often encounter the hypersphere collapse when constructing the model of the hypersphere. To solve this problem, support vector data description based on spherical regularization (SR-SVDD) is proposed in this paper. SR-SVDD applies the idea of support vectors to optimize the learning process by introducing slack terms. Furthermore, this paper proposes weakly supervised support vector data description based on spherical regularization (SR-WSVDD), which utilizes small amounts of labeled data. Ablation experiments and comparison experiments are carried out on MNIST and CIFAR-10. Experimental results show that, compared with supervised algorithms, the performance of SR-WSVDD is improved by 3. 7% on the MNIST, and 16. 7% on the CIFAR-10. In addition, compared with other weakly supervised algorithms, SR-WSVDD improves by 1. 8% on CIFAR-10 dataset. The proposed SR-SVDD solves the spherical collapse of previous Deep SVDD, and makes the anomaly detection results more accurate.
-
Long-term detection and tracking algorithm for moving vessels by maritime UAVs
Fan Yunsheng, Zhang Kai, Niu Longhui, Liu Ting, Fei Fan
Abstract:
An algorithm for long-term maritime target detection and tracking based on the combination of YOLOv5 and ECO_HC is proposed to address the problem of tracking failure caused by occlusion of ship hulls and ships leaving the field of view during unmanned aerial vehicle (UAV) tracking of ship at sea. First, perceptual hashing and the ratio between the second and first major modes are used to comprehensively assess the reliability of the tracking process. In the event of target loss, the YOLOv5 detector is utilized to reposition the target and initialize the tracking model. Thereby, the accumulation of erroneous information is eliminated. Secondly, to address the rotational changes of the target during tracking, the Fourier-Mellin transform is employed for rotation parameter estimation, mitigating performance decline due to target rotation. The proposed algorithm achieves an average precision and success rate of 83. 9% and 76. 7% , respectively, on the OTB-100 dataset. Field experiments of ship tracking in actual maritime scenarios on UAV platforms show precision and success rates of 80. 9% and 60. 4% under complete occlusion, and 90. 2% and 48. 3% when the target is out of the field of view. The experiments demonstrate that the proposed algorithm can effectively suppress the influence of common maritime interference factors.
传感器技术
Industrial Big Data and Intelligent Health Assessment
Precision Measurement Technology and Instrument
Information Processing Technology
Automatic Control Technology
Visual inspection and Image Measurement
AboutOrganizer：China Association for Science and Technology
Governing Body：China Instrument and Control Society
Chief editorial unitf：Zhang Zhonghua
Address：23rd Floor, Building A, Horizon International Tower,No.6 Zhichun Road, Haidian District,Beijing, China
Zip Code：100088
Phone：010-64004400
Email：cjsi@cis.org.cn
ISSN：11-2179/TH