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Roller Bearing Failure Analysis using Gaussian Mixture Models and Convolutional Neural Networks

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Abstract

Rotating machinery failure analysis requires signal preprocessing to extract fault-related information. However, to promote accurate condition monitoring of bearing following two conditions must be achieved (1) intelligent instance annotation and (2) automatic feature extraction and selection. Therefore, an attempt has been made in this paper using Gaussian Mixture Models (GMM) and t-distributed stochastic neighbor embedding (t-SNE) techniques to realize intelligent instance annotation. In addition to this, a one-dimensional convolutional neural network (1DCNN) is utilized for automatic feature extraction and selection. To validate the proposed method, experimentation is conducted on a high-speed rotor-supported bearing test rig to acquire run-to-failure roller bearing lifetime data. Bearing lifetime responses are segregated into different operating conditions, namely normal, slight degradation (SLD), severe degradation (SVD), and failure using t-SNE and GMM techniques. The comparisons are conducted with the existing advanced classifiers based on assessment metrics ROC (receiver operating characteristic) curve, AUC values, precision, recall, and F1-score. The classification accuracy value is obtained as 98.23% for the proposed method, which is highest compared to SVM, KNN, DNN, DBN, LSTM, and BiLSTM classifiers. The comparison results revealed the superior performance of the proposed method compared to existing advanced classifiers. Therefore, the proposed methodology precisely classifies the bearing lifetime data into different operating conditions using vibration acceleration responses.

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Authors and Affiliations

  1. Advanced Mechanical Vibration Lab, Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Maan Singh Rathore & S. P. Harsha

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  1. Maan Singh Rathore
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  2. S. P. Harsha
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Correspondence to S. P. Harsha.

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Appendices

Appendices

Appendix I

See Table 6.

Table 6 Technical Specifications of the test bearing NBC NUC205E
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Appendix II

See Table 7.

Table 7 Statistical time-domain features extracted from vibration responses of rolling element bearing
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Rathore, M.S., Harsha, S.P. Roller Bearing Failure Analysis using Gaussian Mixture Models and Convolutional Neural Networks. J Fail. Anal. and Preven. 22, 1853–1871 (2022). https://doi.org/10.1007/s11668-022-01469-8

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  • DOI: https://doi.org/10.1007/s11668-022-01469-8

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