Instrumentation & Measurement Magazine 24-3 - 49

stationary signals by using some classical fault detection techniques. However, it still remains challenging to extract robust
representative features from nonstationary vibration signals
generated from those defects on rotating bearing components (e.g., the rotating ring and rolling elements) or caused
by variations in bearing dynamics (e.g., different bearings and
support structures) and in operating conditions (e.g., loads
and speeds).
This paper has discussed bearing characteristic frequency calculation and analysis. It has summarized the
considerations in using frequency analysis for bearing fault
detection. In general, signal denoising is necessary to improve the SNR before advanced analysis. However, the
denoising operation should not be over processed in terms
of kurtosis values, which may not guarantee to highlight
fault-related features. The energy kurtosis demodulation
technique has been used as an example to illustrate the approaches for nonstationary signal processing. Some test
results have been used to illustrate how to use signal processing techniques for bearing fault detection.
In the future research and development, new signal processing theories are needed for bearing fault detection under
variable bearing dynamics and time-varying load and speed
conditions. Intelligent classifiers are needed to integrate the
merits of numerous fault detection techniques, historical
data, and expert knowledge for bearing fault detection. The
diagnostic accuracy can be improved by using appropriate
machine learning and deep learning algorithms, to improve
the robustness of the diagnostic system to accommodate variations in bearing system dynamics and operation
conditions.

[5] T. Sengoz, Z. Chan and W. Wang, " A Teager-Kaiser spectrum
technique for bearing fault detection in induction motors, " Mech.
and Mechatronics Eng., vol. 10, no. 5, pp. 18-24, 2019.
[6] S. Osman and W. Wang, " A normalized Hilbert Huang transform
technique for bearing fault detection, " J. Vibration and Control, vol.
22, no. 11, pp. 2771-2787, 2016.
[7] J. Liu, W. Wang, and F. Ma, " Bearing system health condition
monitoring using a wavelet cross-spectrum analysis technique, "
J. Vibration and Control, vol. 18, no. 7, pp.953-963, 2012.
[8] W. Wang and H. Lee, " An energy kurtosis demodulation
technique for signal denoising and bearing fault detection, " Meas.
Sci. and Technol., vol. 24, no. 2, 025601, 2013.
[9] R. Wiggins, " Minimum entropy deconvolution, " Geoexploration,
vol. 16, pp. 21-35, 1978.
[10] L. Cui, J. Wang et al., " Early fault detection method for rolling
bearing based on multiscale morphological filtering of
information entropy threshold, " J. Mech. Sci. and Technol., vol. 33,
pp. 1513-1522, 2019.
[11] N. Sawalhi, R. Randall, and H. Endo, " The enhancement of
fault detection and diagnosis in rolling element bearings using
minimum entropy deconvolution combined with spectral
kurtosis, " Mech. Syst. Signal Process, vol. 21, pp. 2616-2633, 2007.
[12] A. Shukla, M. Mahmud and W. Wang, " A smart sensor-based
monitoring system for vibration measurement and bearing fault
detection, " Meas. Sci. and Technol., vol. 31, no. 10, 105104, 2020.
[13] S. Osman and W. Wang, " A Morphological HHT technique for
bearing fault detection, " IEEE Trans. Instrum. Meas., vol. 65, no. 11,
pp. 2646-2656, 2016.
[14] W. Sui, S. Osman and W. Wang, " An adaptive envelope spectrum
technique for bearing fault detection, " Meas. Sci. and Technol., vol.
25, no. 9, 095004, 2014.
[15] P. Luong and W. Wang, " Smart sensor-based synergistic analysis

References

for rotor bar fault detection of induction motors, " IEEE/ASME

[1] R. Randall, Vibration-Based Condition Monitoring. New York, NY,

Trans. Mechatronics, vol. 25, no. 2, pp. 1067-75, 2020.

USA: Wiley, 2011.
[2] A. Jardine, D. Lin, and D. Banjevic, " A review on machinery
diagnostics and prognostics implementing condition-based
maintenance, " Mech. Sys. Signal Process, vol. 20, pp. 1483-10, 2007.
[3] S. Lu, P. Zheng et al., " Sound-aided vibration weak signal
enhancement for bearing fault detection by using adaptive
stochastic resonance, " J. Sound Vib., vol. 449, pp. 18-29, 2019.
[4] S. Osman and W. Wang, " A leakage-free resonance sparse
decomposition technique for bearing fault detection in
gearboxes, " Meas. Sci. and Technol., vol. 29, no. 3, 035004, 2018.

May 2021

Wilson Wang (wwang3@lakeheadu.ca) joined Lakehead University, Ontario, Canada in 2004, and now he is a Professor in
the Department of Mechanical Engineering. He received his
Ph.D. degree in mechatronics engineering from the University
of Waterloo, Ontario, Canada in 2002. From 2002 to 2004, he was
employed as a Senior Scientist at Mechworks Systems Inc. His research interests include signal processing, artificial intelligence,
machine learning, diagnostics and prognostics of engineering
systems, smart sensors, intelligent control, and mechatronics.

IEEE Instrumentation & Measurement Magazine 49

Instrumentation & Measurement Magazine 24-3

Table of Contents for the Digital Edition of Instrumentation & Measurement Magazine 24-3