Performance evaluation of three signal decomposition methods for bearing fault detection and classification. (March 2023)

• Record Type: Journal Article
• Authors: Mathur, Amit; Kumar, Pradeep; Harsha, S. P.
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 237:Number 1(2023)
• Page Start: 114
• Page End: 130
• Publication Date: 2023-03
• Subjects: Ball bearing -- fault correlation-based index -- variational mode decomposition -- Hausdorff distance -- condition indicators; Machinery, Dynamics of -- Periodicals; Machine design -- Periodicals; 621.815
• DOI: 10.1177/14644193221136661
• Languages: English
• ISSNs: 1464-4193

Abstract

In the present study, the performance evaluation of the signal decomposition methods; variational mode decomposition, empirical mode decomposition, and ensemble empirical mode decomposition, for the ball bearing fault detection and classification for the experimentally recorded vibration signals has been done. This work proposed a novel hybrid sensitive mode selection method combining three statistical measures (energy-based index, fault correlation-based index, and Hausdorff distance-based index) and investigating the effect of the selected sensitive mode extracted by the decomposition methods for the bearing defect frequency detection. The vibration data have been acquired for the healthy and seeded faults of different sizes for the inner and outer raceway defects. The complete features dataset comprises five time-domain, four spectral-domain, and two non-linear statistical features. The k-Nearest Neighbor, Support Vector Machine, and Naive Bayes classifiers are used for fault classification and predict the results with four performance metrics: accuracy, sensitivity, precision, and F-score. Firstly, the results of signal decomposition employing hybrid sensitive mode functions and statistical analysis of condition indicators (RMS, kurtosis and crest factor) revealed that the VMD outperforms the other two techniques. Secondly, the fault classification results predicted that the k-Nearest Neighbor classifier outperforms the other two classifiers. This proposed novel sensitive mode selection method significantly improves the bearing fault classification performance metrics with the features extracted from the selective mode functions with all three decomposition methods.