Hybrid Method with Parallel-Factor Theory, a Support Vector Machine, and Particle Filter Optimization for Intelligent Machinery Failure Identification
Here, a novel hybrid method of intelligent fault identification within complex mechanical systems was proposed using parallel-factor (PARAFAC) theory and adaptive particle swarm optimization (APSO) for a support vector machine (SVM). The parallel-factor multi-scale analysis theory was studied to rec...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/11/8/837 |
| _version_ | 1797584064045121536 |
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| author | Shaoyi Li Hanxin Chen Yongting Chen Yunwei Xiong Ziwei Song |
| author_facet | Shaoyi Li Hanxin Chen Yongting Chen Yunwei Xiong Ziwei Song |
| author_sort | Shaoyi Li |
| collection | DOAJ |
| description | Here, a novel hybrid method of intelligent fault identification within complex mechanical systems was proposed using parallel-factor (PARAFAC) theory and adaptive particle swarm optimization (APSO) for a support vector machine (SVM). The parallel-factor multi-scale analysis theory was studied to reconstruct tensor feature information based on a three-dimensional matrix for time, frequency, and spatial vectors. A multi-scale wavelet analysis was used to transform the original multi-channel experimental data acquired from a gearbox into a three-dimensional feature matrix of the multi-level structure. The optimal correspondence among the two-dimensional feature signals in the frequency and time domains for the different fault modes was established by the PARAFAC theory. An intelligent APSO algorithm was developed to obtain the optimal parameter structures of an SVM classifier. A comparison with the existing time–frequency analysis method showed that the proposed hybrid PARAFAC-PSO-SVM diagnosis model effectively eliminated the redundant information in the multi-dimensional tensor features but retained the important components. The PARAFAC-APSO-SVM hybrid diagnostic model achieved fast, accurate, and simple fault-classification and identification results, and could provide theoretical support for the application of the PARAFAC theory to complex mechanical fault diagnosis. |
| first_indexed | 2024-03-10T23:47:57Z |
| format | Article |
| id | doaj.art-e117522b860145dd9d7cf08d23cf2274 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T23:47:57Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-e117522b860145dd9d7cf08d23cf22742023-11-19T01:57:23ZengMDPI AGMachines2075-17022023-08-0111883710.3390/machines11080837Hybrid Method with Parallel-Factor Theory, a Support Vector Machine, and Particle Filter Optimization for Intelligent Machinery Failure IdentificationShaoyi Li0Hanxin Chen1Yongting Chen2Yunwei Xiong3Ziwei Song4School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430074, ChinaSchool of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430074, ChinaTandon School of Engineering, New York University, New York, NY 11201, USASchool of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430074, ChinaSchool of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430074, ChinaHere, a novel hybrid method of intelligent fault identification within complex mechanical systems was proposed using parallel-factor (PARAFAC) theory and adaptive particle swarm optimization (APSO) for a support vector machine (SVM). The parallel-factor multi-scale analysis theory was studied to reconstruct tensor feature information based on a three-dimensional matrix for time, frequency, and spatial vectors. A multi-scale wavelet analysis was used to transform the original multi-channel experimental data acquired from a gearbox into a three-dimensional feature matrix of the multi-level structure. The optimal correspondence among the two-dimensional feature signals in the frequency and time domains for the different fault modes was established by the PARAFAC theory. An intelligent APSO algorithm was developed to obtain the optimal parameter structures of an SVM classifier. A comparison with the existing time–frequency analysis method showed that the proposed hybrid PARAFAC-PSO-SVM diagnosis model effectively eliminated the redundant information in the multi-dimensional tensor features but retained the important components. The PARAFAC-APSO-SVM hybrid diagnostic model achieved fast, accurate, and simple fault-classification and identification results, and could provide theoretical support for the application of the PARAFAC theory to complex mechanical fault diagnosis.https://www.mdpi.com/2075-1702/11/8/837parallel factorsfault diagnosisSVMAPSOhybrid diagnosis model |
| spellingShingle | Shaoyi Li Hanxin Chen Yongting Chen Yunwei Xiong Ziwei Song Hybrid Method with Parallel-Factor Theory, a Support Vector Machine, and Particle Filter Optimization for Intelligent Machinery Failure Identification Machines parallel factors fault diagnosis SVM APSO hybrid diagnosis model |
| title | Hybrid Method with Parallel-Factor Theory, a Support Vector Machine, and Particle Filter Optimization for Intelligent Machinery Failure Identification |
| title_full | Hybrid Method with Parallel-Factor Theory, a Support Vector Machine, and Particle Filter Optimization for Intelligent Machinery Failure Identification |
| title_fullStr | Hybrid Method with Parallel-Factor Theory, a Support Vector Machine, and Particle Filter Optimization for Intelligent Machinery Failure Identification |
| title_full_unstemmed | Hybrid Method with Parallel-Factor Theory, a Support Vector Machine, and Particle Filter Optimization for Intelligent Machinery Failure Identification |
| title_short | Hybrid Method with Parallel-Factor Theory, a Support Vector Machine, and Particle Filter Optimization for Intelligent Machinery Failure Identification |
| title_sort | hybrid method with parallel factor theory a support vector machine and particle filter optimization for intelligent machinery failure identification |
| topic | parallel factors fault diagnosis SVM APSO hybrid diagnosis model |
| url | https://www.mdpi.com/2075-1702/11/8/837 |
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