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Adaptive Variational Mode Decomposition for Bearing Fault Detection

Journal of signal and Information Processing 2023年第2期14卷 9-24页

作者： Xing Xing Ming Zhang Wilson Wang Electrical and Computer Engineering Lakehead University Thunder Bay Canada Automotive Engineering Weifang College of Engineering Qingzhou China Mechanical Engineering Lakehead University Thunder Bay Canada

Rolling element bearings are commonly used in rotary mechanical and electrical equipment. According to investigation, more than half of rotating machinery defects are related to bearing faults. However, reliable bearing fault detection still remains a challenging task, especially in industrial applications. The objective of this work is to propose an adaptive variational mode decomposition (AVMD) technique for non-stationary signal analysis and bearing fault detection. The AVMD includes several steps in processing: 1) signal characteristics are analyzed to determine the signal center frequency and the related parameters. 2) The ensemble-kurtosis index is suggested to decompose the target signal and select the most representative intrinsic mode functions (IMFs). 3) The envelope spectrum analysis is performed using the selected IMFs to identify the characteristic features for bearing fault detection. The effectiveness of the proposed AVMD technique is examined by experimental tests under different bearing conditions, with the comparison of other related bearing fault techniques.

关键词： Bearing Fault Detection vibration signal analysis Intrinsic Mode Functions Variational Mode Decomposition

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Bone cutting status detection based on vibration sensor array

Bone cutting status detection based on vibration sensor arra...

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第40届中国控制会议

作者： Jinggang Wang Guangming Xia Yu Dai Jianxun Zhang College of Artificial Intelligence Nankai University

It is necessary to accurately and stably monitor the feedback signal during the bone milling process so that the surgical robot can better control the *** response to this problem,this article uses a vibration sensor fixed on the milling cutter for signal *** the drilling process,the milling cutter will produce different amplitudes of vibration under different conditions,the vibration signal collected by the acceleration sensor array is subjected to Fast Fourier Transform（FFT） and frequency domain features are extracted,and then the milling state is monitored through the BP neural *** show that the recognition rate of milling cortical bone and cancellous bone reaches 100%;Under the condition of cancellous bone,the recognition accuracy of the milling depth into three states of 0.5 mm,0.8 mm and,1.2 mm are all above 100%.The comparison with a single sensor proves that the sensor array has better accuracy and stability in the robot milling process.

关键词： Bone cutting FFT surgical robot sensor array vibration signal analysis

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