Performance evaluation of BPSO & PCA as feature reduction techniques for bearing fault diagnosis
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...
| Main Authors: | , , |
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| Format: | Book Chapter |
| Language: | English English |
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Springer Nature Singapore
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/31926/ http://umpir.ump.edu.my/id/eprint/31926/1/PerformanceEvaluationofBPSOPCAasFeatureReductionTechniquesforBearingFaultDiagnosis-3.pdf http://umpir.ump.edu.my/id/eprint/31926/7/Performance%20Evaluation%20of%20BPSO%20%26%20PCA%20as%20Feature%20Reduction%20Techniques%20for%20Bearing%20Fault%20Diagnosis.docx |
| _version_ | 1848823892559265792 |
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| author | Faysal, Atik Ngui, Wai Keng Lim, M. H. |
| author_facet | Faysal, Atik Ngui, Wai Keng Lim, M. H. |
| author_sort | Faysal, Atik |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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 |
| first_indexed | 2025-11-15T03:04:21Z |
| format | Book Chapter |
| id | ump-31926 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:04:21Z |
| publishDate | 2021 |
| publisher | Springer Nature Singapore |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-319262022-11-03T07:10:10Z http://umpir.ump.edu.my/id/eprint/31926/ Performance evaluation of BPSO & PCA as feature reduction techniques for bearing fault diagnosis Faysal, Atik Ngui, Wai Keng Lim, M. H. TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery 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 Springer Nature Singapore 2021-07-16 Book Chapter PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/31926/1/PerformanceEvaluationofBPSOPCAasFeatureReductionTechniquesforBearingFaultDiagnosis-3.pdf pdf en http://umpir.ump.edu.my/id/eprint/31926/7/Performance%20Evaluation%20of%20BPSO%20%26%20PCA%20as%20Feature%20Reduction%20Techniques%20for%20Bearing%20Fault%20Diagnosis.docx Faysal, Atik and Ngui, Wai Keng and Lim, M. H. (2021) Performance evaluation of BPSO & PCA as feature reduction techniques for bearing fault diagnosis. In: Recent Trends in Mechatronics Towards Industry 4.0. Lecture Notes in Electrical Engineering, 730 . Springer Nature Singapore, Singapore, pp. 605-615. ISBN 978-981-33-4596-6 (Print) 978-981-33-4596-6 (Online) https://doi.org/10.1007/978-981-33-4597-3_55 https://doi.org/10.1007/978-981-33-4597-3_55 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery Faysal, Atik Ngui, Wai Keng Lim, M. H. Performance evaluation of BPSO & PCA as feature reduction techniques for bearing fault diagnosis |
| title | Performance evaluation of BPSO & PCA as feature reduction techniques for bearing fault diagnosis |
| title_full | Performance evaluation of BPSO & PCA as feature reduction techniques for bearing fault diagnosis |
| title_fullStr | Performance evaluation of BPSO & PCA as feature reduction techniques for bearing fault diagnosis |
| title_full_unstemmed | Performance evaluation of BPSO & PCA as feature reduction techniques for bearing fault diagnosis |
| title_short | Performance evaluation of BPSO & PCA as feature reduction techniques for bearing fault diagnosis |
| title_sort | performance evaluation of bpso & pca as feature reduction techniques for bearing fault diagnosis |
| topic | TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/31926/ http://umpir.ump.edu.my/id/eprint/31926/ http://umpir.ump.edu.my/id/eprint/31926/ http://umpir.ump.edu.my/id/eprint/31926/1/PerformanceEvaluationofBPSOPCAasFeatureReductionTechniquesforBearingFaultDiagnosis-3.pdf http://umpir.ump.edu.my/id/eprint/31926/7/Performance%20Evaluation%20of%20BPSO%20%26%20PCA%20as%20Feature%20Reduction%20Techniques%20for%20Bearing%20Fault%20Diagnosis.docx |