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Multiclass Fault Classification of an Induction Motor Bearing Vibration Data Using Wavelet Packet Transform Features and Artificial Intelligence

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Abstract

Purpose

The bearing that supports the rotor shaft is one of the essential aspects of any spinning machine, particularly in induction motors. Maintaining the bearing condition with some degree of assurance is essential.

Methods

In the present research, a two-level Wavelet Packet transform (WPT) has been employed for the filtration to investigate the meaningful vibration signal. An ANOVA F test and Mutual information method have been used for feature selection. The Logistic Regression (LR) and Support Vector Classifier (SVC) has been considered to classify the fault.

Results

Eleven statistical features from each of the original signals and wavelet decomposed signals were calculated. The present work investigates the existence of a fault, the type of fault, and its severity. The LR and SVC Model are used to evaluate the performance of the optimum feature set obtained from feature selection techniques.

Conclusion

The sub-band signal DD2 with SVC gives the best results for all three cases from the results obtained with the full set feature as compared to the LR technique. The grid search method along with SVC produced the greatest results, with three features provided. Thus the classification accuracy of 100 percent was achieved with only three features in the case of two classes, a maximum accuracy of 96.3% was obtained from four classes with optimal feature 8, and an accuracy of 94.6% with optimal 8 features for 10 class problems. Thus the present technique for bearing fault diagnosis can be implemented for practical purposes.

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Acknowledgements

The authors would like to express their gratitude to Prof. KA Loparo and Case Western Reserve University for making the bearing data set accessible and granting permission to use it.

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Authors and Affiliations

  1. Rajkiya Engineering College, Kannauj, India

    Shilpi Yadav

  2. Rajkiya Engineering College, Sonbhadra, India

    Raj Kumar Patel & Vijay Pratap Singh

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  1. Shilpi Yadav
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  2. Raj Kumar Patel
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Correspondence to Shilpi Yadav.

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Yadav, S., Patel, R.K. & Singh, V.P. Multiclass Fault Classification of an Induction Motor Bearing Vibration Data Using Wavelet Packet Transform Features and Artificial Intelligence. J. Vib. Eng. Technol. 11, 3093–3108 (2023). https://doi.org/10.1007/s42417-022-00733-3

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