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Shock and Vibration
Volume 2015 (2015), Article ID 486159, 12 pages
Research Article

Automatic Condition Monitoring of Industrial Rolling-Element Bearings Using Motor’s Vibration and Current Analysis

Department of Energy Technology, Aalborg University, Esbjerg Campus, Niels Bohrs Vej 8, 6700 Esbjerg, Denmark

Received 26 January 2015; Accepted 8 March 2015

Academic Editor: Jiawei Xiang

Copyright © 2015 Zhenyu Yang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An automatic condition monitoring for a class of industrial rolling-element bearings is developed based on the vibration as well as stator current analysis. The considered fault scenarios include a single-point defect, multiple-point defects, and a type of distributed defect. Motivated by the potential commercialization, the developed system is promoted mainly using off-the-shelf techniques, that is, the high-frequency resonance technique with envelope detection and the average of short-time Fourier transform. In order to test the flexibility and robustness, the monitoring performance is extensively studied under diverse operating conditions: different sensor locations, motor speeds, loading conditions, and data samples from different time segments. The experimental results showed the powerful capability of vibration analysis in the bearing point defect fault diagnosis. The current analysis also showed a moderate capability in diagnosis of point defect faults depending on the type of fault, severity of the fault, and the operational condition. The temporal feature indicated a feasibility to detect generalized roughness fault. The practical issues, such as deviations of predicted characteristic frequencies, sideband effects, time-average of spectra, and selection of fault index and thresholds, are also discussed. The experimental work shows a huge potential to use some simple methods for successful diagnosis of industrial bearing systems.