Fault Diagnosis Based on Multi-Scale Redefined Dimensionless Indicators and Density Peak Clustering With Geodesic Distances
A novel fault diagnosis method for rolling bearings based on multi-scale redefined dimensionless indicators and an unsupervised feature selection method using density peak clustering with geodesic distances is proposed in this paper. First, a new feature extraction method is proposed based on redefi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9075995/ |
| _version_ | 1818578215672741888 |
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| author | Qin Hu Qi Zhang Xiao-Sheng Si Ai-Song Qin Qing-Hua Zhang |
| author_facet | Qin Hu Qi Zhang Xiao-Sheng Si Ai-Song Qin Qing-Hua Zhang |
| author_sort | Qin Hu |
| collection | DOAJ |
| description | A novel fault diagnosis method for rolling bearings based on multi-scale redefined dimensionless indicators and an unsupervised feature selection method using density peak clustering with geodesic distances is proposed in this paper. First, a new feature extraction method is proposed based on redefined dimensionless indicators and multi-scale analysis called multi-scale redefined dimensionless indicators. Then, density peak clustering with geodesic distances is utilized to automatically find the important multi-scale redefined dimensionless indicators. To the best of our knowledge, this is the first study to use density peak clustering with geodesic distances to explore unsupervised feature selection in the fault diagnosis field. Finally, the selected multi-scale redefined dimensionless indicators are fed into a quadratic discriminant analysis classifier to simultaneously identify 12 different conditions of rolling bearings. Experimental results demonstrated that the proposed method can successfully differentiate 12 localized fault types, fault severities, and fault orientations of rolling bearings. |
| first_indexed | 2024-12-16T06:42:16Z |
| format | Article |
| id | doaj.art-49f00d010601420eb1803188913277d1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T06:42:16Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-49f00d010601420eb1803188913277d12022-12-21T22:40:38ZengIEEEIEEE Access2169-35362020-01-018847778479110.1109/ACCESS.2020.29894609075995Fault Diagnosis Based on Multi-Scale Redefined Dimensionless Indicators and Density Peak Clustering With Geodesic DistancesQin Hu0https://orcid.org/0000-0002-6446-1151Qi Zhang1https://orcid.org/0000-0002-1387-0289Xiao-Sheng Si2https://orcid.org/0000-0001-5226-9923Ai-Song Qin3https://orcid.org/0000-0002-7365-4502Qing-Hua Zhang4Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, ChinaDepartment of Automation, Rocket Force University of Engineering, Xi’an, ChinaDepartment of Automation, Rocket Force University of Engineering, Xi’an, ChinaGuangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, ChinaGuangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, ChinaA novel fault diagnosis method for rolling bearings based on multi-scale redefined dimensionless indicators and an unsupervised feature selection method using density peak clustering with geodesic distances is proposed in this paper. First, a new feature extraction method is proposed based on redefined dimensionless indicators and multi-scale analysis called multi-scale redefined dimensionless indicators. Then, density peak clustering with geodesic distances is utilized to automatically find the important multi-scale redefined dimensionless indicators. To the best of our knowledge, this is the first study to use density peak clustering with geodesic distances to explore unsupervised feature selection in the fault diagnosis field. Finally, the selected multi-scale redefined dimensionless indicators are fed into a quadratic discriminant analysis classifier to simultaneously identify 12 different conditions of rolling bearings. Experimental results demonstrated that the proposed method can successfully differentiate 12 localized fault types, fault severities, and fault orientations of rolling bearings.https://ieeexplore.ieee.org/document/9075995/Feature extractionunsupervised learningnearest neighbor searchesclustering algorithmsmechanical engineeringvibration measurement |
| spellingShingle | Qin Hu Qi Zhang Xiao-Sheng Si Ai-Song Qin Qing-Hua Zhang Fault Diagnosis Based on Multi-Scale Redefined Dimensionless Indicators and Density Peak Clustering With Geodesic Distances IEEE Access Feature extraction unsupervised learning nearest neighbor searches clustering algorithms mechanical engineering vibration measurement |
| title | Fault Diagnosis Based on Multi-Scale Redefined Dimensionless Indicators and Density Peak Clustering With Geodesic Distances |
| title_full | Fault Diagnosis Based on Multi-Scale Redefined Dimensionless Indicators and Density Peak Clustering With Geodesic Distances |
| title_fullStr | Fault Diagnosis Based on Multi-Scale Redefined Dimensionless Indicators and Density Peak Clustering With Geodesic Distances |
| title_full_unstemmed | Fault Diagnosis Based on Multi-Scale Redefined Dimensionless Indicators and Density Peak Clustering With Geodesic Distances |
| title_short | Fault Diagnosis Based on Multi-Scale Redefined Dimensionless Indicators and Density Peak Clustering With Geodesic Distances |
| title_sort | fault diagnosis based on multi scale redefined dimensionless indicators and density peak clustering with geodesic distances |
| topic | Feature extraction unsupervised learning nearest neighbor searches clustering algorithms mechanical engineering vibration measurement |
| url | https://ieeexplore.ieee.org/document/9075995/ |
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