A Novel Fault Diagnosis Method Based on Integrating Empirical Wavelet Transform and Fuzzy Entropy for Motor Bearing

• Published in: IEEE access Vol. 6; pp. 35042 - 35056
• Main Authors: Deng, Wu, Zhang, Shengjie, Zhao, Huimin, Yang, Xinhua
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AM-FM components; Complexity; Empirical analysis; empirical wavelet transform; Entropy; Fault diagnosis; Feature extraction; Fourier spectrum segmentation; fuzzy entropy; Hilbert transformation; Motor bearing; Pattern recognition; Signal analysis; Signal processing; support vector machine; Support vector machines; Transmissions (machine elements); Vibration analysis; Vibration measurement; Vibrations; Wavelet transforms

Motor bearing is subjected to the joint effects of much more loads, transmissions, and shocks that cause bearing fault and machinery breakdown. A vibration signal analysis method is the most popular technique that is used to monitor and diagnose the fault of motor bearing. However, the application o...