Research on feature extraction and intelligent diagnosis method of reciprocating compressor bearing clearance fault
Abstract This study presents an innovative approach to fault diagnosis in sliding bearings, targeting the challenges posed by weak fault signals and heavy noise interference. The proposed method employs a generalized multi-scale permutation entropy (GMPE) algorithm, which utilizes a multi-scale mean...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-06-01
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| Series: | Journal of Engineering and Applied Science |
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| Online Access: | https://doi.org/10.1186/s44147-025-00667-z |
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| _version_ | 1839648102309429248 |
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| author | Feng Yan |
| author_facet | Feng Yan |
| author_sort | Feng Yan |
| collection | DOAJ |
| description | Abstract This study presents an innovative approach to fault diagnosis in sliding bearings, targeting the challenges posed by weak fault signals and heavy noise interference. The proposed method employs a generalized multi-scale permutation entropy (GMPE) algorithm, which utilizes a multi-scale mean coarse-graining strategy to effectively capture dynamic transitions in signals. To overcome the shortcomings of traditional binary tree support vector machine (BTSVM) classifiers—such as slow convergence and error accumulation due to early misclassifications—an enhanced BTSVM model is introduced to reduce error propagation. The effectiveness of the method is validated on both reciprocating compressor sliding bearings and automotive rolling bearings, achieving a fault diagnosis accuracy of over 99%. These results highlight a significant advancement in mechanical fault detection and demonstrate the strong potential of combining GMPE with an improved BTSVM for accurate fault diagnosis in complex machinery. |
| format | Article |
| id | doaj-art-3639734480c545dbaa792c2acf65d3c2 |
| institution | Matheson Library |
| issn | 1110-1903 2536-9512 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Engineering and Applied Science |
| spelling | doaj-art-3639734480c545dbaa792c2acf65d3c22025-06-29T11:11:07ZengSpringerOpenJournal of Engineering and Applied Science1110-19032536-95122025-06-0172111910.1186/s44147-025-00667-zResearch on feature extraction and intelligent diagnosis method of reciprocating compressor bearing clearance faultFeng Yan0Hunan Mechanical & Electrical PolytechnicAbstract This study presents an innovative approach to fault diagnosis in sliding bearings, targeting the challenges posed by weak fault signals and heavy noise interference. The proposed method employs a generalized multi-scale permutation entropy (GMPE) algorithm, which utilizes a multi-scale mean coarse-graining strategy to effectively capture dynamic transitions in signals. To overcome the shortcomings of traditional binary tree support vector machine (BTSVM) classifiers—such as slow convergence and error accumulation due to early misclassifications—an enhanced BTSVM model is introduced to reduce error propagation. The effectiveness of the method is validated on both reciprocating compressor sliding bearings and automotive rolling bearings, achieving a fault diagnosis accuracy of over 99%. These results highlight a significant advancement in mechanical fault detection and demonstrate the strong potential of combining GMPE with an improved BTSVM for accurate fault diagnosis in complex machinery.https://doi.org/10.1186/s44147-025-00667-zGeneralized multi-scale permutation entropyPattern recognitionPlain bearingFault diagnosis |
| spellingShingle | Feng Yan Research on feature extraction and intelligent diagnosis method of reciprocating compressor bearing clearance fault Journal of Engineering and Applied Science Generalized multi-scale permutation entropy Pattern recognition Plain bearing Fault diagnosis |
| title | Research on feature extraction and intelligent diagnosis method of reciprocating compressor bearing clearance fault |
| title_full | Research on feature extraction and intelligent diagnosis method of reciprocating compressor bearing clearance fault |
| title_fullStr | Research on feature extraction and intelligent diagnosis method of reciprocating compressor bearing clearance fault |
| title_full_unstemmed | Research on feature extraction and intelligent diagnosis method of reciprocating compressor bearing clearance fault |
| title_short | Research on feature extraction and intelligent diagnosis method of reciprocating compressor bearing clearance fault |
| title_sort | research on feature extraction and intelligent diagnosis method of reciprocating compressor bearing clearance fault |
| topic | Generalized multi-scale permutation entropy Pattern recognition Plain bearing Fault diagnosis |
| url | https://doi.org/10.1186/s44147-025-00667-z |
| work_keys_str_mv | AT fengyan researchonfeatureextractionandintelligentdiagnosismethodofreciprocatingcompressorbearingclearancefault |