Faysal, Atik and Ngui, Wai Keng and M. H., Lim (2022) Performance Evaluation of BPSO & PCA as Feature Reduction Techniques for Bearing Fault Diagnosis. In: Recent Trends in Mechatronics Towards Industry 4.0: Selected Articles from iM3F 2020, Malaysia , 6 August 2020 , Virtual Conference, Universiti Malaysia Pahang, Malaysia. pp. 605-615., 730.
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Abstract
Vibration-based signal processing is the most popular and effective approach for fault diagnosis of bearing. In this paper, time-frequency domain analysis, i.e. empirical mode decomposition (EMD) was applied to the raw vibration signal. Intrinsic mode function (IMF) containing the characteristics of vibration data was analysed to obtain 90 statistical features. Two feature reduction algorithms, namely principal components analysis (PCA) and binary particle swarm optimiser (BPSO) were applied individually for feature reduction. The reduced feature subsets were 12 and 35 for PCA and BPSO, respectively. K-Nearest Neighbours (K-NN) was used as an intelligent method for fault diagnosis. K-NN was applied to the entire feature set and individually on the selected feature subset of PCA and BPSO. The reduced feature subset with PCA performed the finest in all the measurements taken. For BPSO, although it effectively reduced the feature dimension and classification time, the testing accuracy was slightly lower. Comparing the output accuracy of the K-NN classifier for the selected methods demonstrated the effectiveness of PCA and BPSO as efficacious feature reduction techniques.
| Item Type: | Conference or Workshop Item (Lecture) |
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| Additional Information: | Lecture Notes in Electrical Engineering |
| Uncontrolled Keywords: | Feature reduction, Principal components analysis, Binary particle swarm optimiser, Empirical mode decomposition |
| Subjects: | T Technology > TJ Mechanical engineering and machinery |
| Faculty/Division: | Faculty of Mechanical and Automotive Engineering Technology |
| Depositing User: | Mr. Ngui Wai Keng |
| Date Deposited: | 03 Nov 2022 03:27 |
| Last Modified: | 03 Nov 2022 03:27 |
| URI: | http://umpir.ump.edu.my/id/eprint/35518 |
| Download Statistic: | View Download Statistics |
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