The components which often fail in a rolling element bearing are the outer-race, the inner-race, the rollers, and the cage. Such failures generate a series of impact vibrations in short time intervals, which occur at Bearing Characteristic Frequencies (BCF). Since BCF contain very little energy, and are usually overwhelmed by noise and higher levels of macro-structural vibrations, they are difficult to find in their frequency spectra when using the common technique of Fast Fourier Transforms (FFT). Therefore, Envelope Detection (ED) is always used with FFT to identify faults occurring at the BCF. However, the computation of ED is complicated, and requires expensive equipment and experienced operators to process. This, coupled with the incapacity of FFT to detect nonstationary signals, makes wavelet analysis a popular alternative for machine fault diagnosis. Wavelet analysis provides multi-resolution in time-frequency distribution for easier detection of abnormal vibration signals. From the results of extensive experiments performed in a series of motor-pump driven systems, the methods of wavelet analysis and FFT with ED are proven to be efficient in detecting some types of bearing faults. Since wavelet analysis can detect both periodic and nonperiodic signals, it allows the machine operator to more easily detect the remaining types of bearing faults which are impossible by the method of FFT with ED. Hence, wavelet analysis is a better fault diagnostic tool for the practice in maintenance.
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e-mail: meptse@cityu.edu.hk
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July 2001
Technical Papers
Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities
Peter W. Tse, Mem. ASME,
e-mail: meptse@cityu.edu.hk
Peter W. Tse, Mem. ASME
Smart Asset Management Laboratory, Department of Manufacturing Engineering & Engineering Management, City University of Hong Kong, Tat Chee Ave., Hong Kong
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Y. H. Peng, Mem. ASME,
Y. H. Peng, Mem. ASME
Smart Asset Management Laboratory, Department of Manufacturing Engineering & Engineering Management, City University of Hong Kong, Tat Chee Ave., Hong Kong
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Richard Yam
Richard Yam
Smart Asset Management Laboratory, Department of Manufacturing Engineering & Engineering Management, City University of Hong Kong, Tat Chee Ave., Hong Kong
Search for other works by this author on:
Peter W. Tse, Mem. ASME
Smart Asset Management Laboratory, Department of Manufacturing Engineering & Engineering Management, City University of Hong Kong, Tat Chee Ave., Hong Kong
e-mail: meptse@cityu.edu.hk
Y. H. Peng, Mem. ASME
Smart Asset Management Laboratory, Department of Manufacturing Engineering & Engineering Management, City University of Hong Kong, Tat Chee Ave., Hong Kong
Richard Yam
Smart Asset Management Laboratory, Department of Manufacturing Engineering & Engineering Management, City University of Hong Kong, Tat Chee Ave., Hong Kong
Contributed by the Technical Committee on Vibration and Sound for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received April 2000; revised Mar. 2001. Associate Editor: G. T. Flowers.
J. Vib. Acoust. Jul 2001, 123(3): 303-310 (8 pages)
Published Online: March 1, 2001
Article history
Received:
April 1, 2000
Revised:
March 1, 2001
Citation
Tse, P. W., Peng, Y. H., and Yam, R. (March 1, 2001). "Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities ." ASME. J. Vib. Acoust. July 2001; 123(3): 303–310. https://doi.org/10.1115/1.1379745
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