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Support vector machines based non-contact fault diagnosis system for bearings

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Abstract

Bearing defects have been accepted as one of the major causes of failure in rotating machinery. It is important to identify and diagnose the failure behavior of bearings for the reliable operation of equipment. In this paper, a low-cost non-contact vibration sensor has been developed for detecting the faults in bearings. The supervised learning method, support vector machine (SVM), has been employed as a tool to validate the effectiveness of the developed sensor. Experimental vibration data collected for different bearing defects under various loading and running conditions have been analyzed to develop a system for diagnosing the faults for machine health monitoring. Fault diagnosis has been accomplished using discrete wavelet transform for denoising the signal. Mahalanobis distance criteria has been employed for selecting the strongest feature on the extracted relevant features. Finally, these selected features have been passed to the SVM classifier for identifying and classifying the various bearing defects. The results reveal that the vibration signatures obtained from developed non-contact sensor compare well with the accelerometer data obtained under the same conditions. A developed sensor is a promising tool for detecting the bearing damage and identifying its class. SVM results have established the effectiveness of the developed non-contact sensor as a vibration measuring instrument which makes the developed sensor a cost-effective tool for the condition monitoring of rotating machines.

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Acknowledgements

The authors acknowledge the financial support from Department of Science and Technology (DST), New Delhi, India [Grant No. IF160851], the Specialized Research Fund for Doctoral program under the AORC scheme of the INSPIRE Program.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, 160019, India

    Deepam Goyal, B. S. Pabla & S. S. Dhami

  2. Department of Electrical Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, 160019, India

    Anurag Choudhary

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  1. Deepam Goyal
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  2. Anurag Choudhary
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  4. S. S. Dhami
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Correspondence to Deepam Goyal.

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Goyal, D., Choudhary, A., Pabla, B.S. et al. Support vector machines based non-contact fault diagnosis system for bearings. J Intell Manuf 31, 1275–1289 (2020). https://doi.org/10.1007/s10845-019-01511-x

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