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Degradation Pattern of High Speed Roller Bearings Using a Data-Driven Deep Learning Approach

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Abstract

In this paper, a data-driven approach is utilized for bearing condition monitoring involving the classification of different operating states by processing the raw vibration data. The vibration responses are analyzed and preprocessed before input to 1D-RCNN (one-dimensional residual convolutional neural network). The comparison results are based on commonly implemented evaluation indices such as precision, recall, F1-score, and ROC plots. Hence, the results revealed the superiority of the proposed methodology and its efficacy in segregating the bearing lifetime data into different operating conditions. Furthermore, t-SNE (t-distributed stochastic neighbor embedding) technique is implemented to represent the precise discriminative learning ability of different layers of the network. The overall classification accuracy values are obtained as 97.2% for 1D-RCNN, 95.31% for 1D-CNN, 86.2%, 86.42%, and 87.4% for SVM, KNN, and DNN, respectively. Hence, the proposed methodology may be effectively implemented for bearing health monitoring utilizing deep learning networks as classifiers.

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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, 247667, 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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Appendix I

Appendix I

Table 3 Particulars of the dimension of test bearing.
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Rathore, M.S., Harsha, S.P. Degradation Pattern of High Speed Roller Bearings Using a Data-Driven Deep Learning Approach. J Sign Process Syst 94, 1557–1568 (2022). https://doi.org/10.1007/s11265-022-01761-8

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  • DOI: https://doi.org/10.1007/s11265-022-01761-8

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