Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm
Rolling bearings are an important part of rotating machinery, and are of great significance for fault diagnosis and life monitoring of rolling bearings. Analyzing fault signals, extracting effective degradation information and establishing corresponding models are the premise of residual life predic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/10/9/729 |
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| author | Xiao Li Songyang An Yuanyuan Shi Yizhe Huang |
| author_facet | Xiao Li Songyang An Yuanyuan Shi Yizhe Huang |
| author_sort | Xiao Li |
| collection | DOAJ |
| description | Rolling bearings are an important part of rotating machinery, and are of great significance for fault diagnosis and life monitoring of rolling bearings. Analyzing fault signals, extracting effective degradation information and establishing corresponding models are the premise of residual life prediction of rolling bearings. In this paper, first, the time-domain features were extracted to form the eigenvector of the vibration signal, and then the index representing the bearing degradation was found. It was found that the time-domain index could effectively describe the degradation information of the bearing, and the multi-dimensional time-domain characteristic information could effectively describe the attenuation trend of the vibration signal of the rolling bearing. On this basis, appropriate feature vectors were selected to describe the degradation characteristics of bearings. Aiming at the problems of large amounts of data, large amounts of information redundancy and unclear performance index of multi-dimensional feature vectors, the dimensionality of multi-dimensional feature vectors was reduced with principal component analysis, thus, simplifying the multi-dimensional feature vectors and reducing the information redundancy. Finally, in view of the support vector machine (SVM)’s needs to determine kernel function parameters and penalty factors, the squirrel optimization algorithm (SOA) was used to adaptively select parameters and establish the state-life evaluation model of rolling bearings. In addition, mean absolute error (MAE) and root mean squared error (RMSE) were used to comprehensively evaluate SOA. The results showed that the SOA reduced the errors by 5.1% and 13.6%, respectively, compared with a genetic algorithm (GA). Compared with particle swarm optimization (PSO), the error of SOA was reduced by 7.6% and 15.9%, respectively. It showed that SOA-SVM effectively improved the adaptability and regression performance of SVM, thus, significantly improving the prediction accuracy. |
| first_indexed | 2024-03-09T23:21:39Z |
| format | Article |
| id | doaj.art-af72edcf9b9641e7a574742fcea04fb3 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T23:21:39Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-af72edcf9b9641e7a574742fcea04fb32023-11-23T17:25:51ZengMDPI AGMachines2075-17022022-08-0110972910.3390/machines10090729Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM AlgorithmXiao Li0Songyang An1Yuanyuan Shi2Yizhe Huang3School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaSchool of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaState Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaRolling bearings are an important part of rotating machinery, and are of great significance for fault diagnosis and life monitoring of rolling bearings. Analyzing fault signals, extracting effective degradation information and establishing corresponding models are the premise of residual life prediction of rolling bearings. In this paper, first, the time-domain features were extracted to form the eigenvector of the vibration signal, and then the index representing the bearing degradation was found. It was found that the time-domain index could effectively describe the degradation information of the bearing, and the multi-dimensional time-domain characteristic information could effectively describe the attenuation trend of the vibration signal of the rolling bearing. On this basis, appropriate feature vectors were selected to describe the degradation characteristics of bearings. Aiming at the problems of large amounts of data, large amounts of information redundancy and unclear performance index of multi-dimensional feature vectors, the dimensionality of multi-dimensional feature vectors was reduced with principal component analysis, thus, simplifying the multi-dimensional feature vectors and reducing the information redundancy. Finally, in view of the support vector machine (SVM)’s needs to determine kernel function parameters and penalty factors, the squirrel optimization algorithm (SOA) was used to adaptively select parameters and establish the state-life evaluation model of rolling bearings. In addition, mean absolute error (MAE) and root mean squared error (RMSE) were used to comprehensively evaluate SOA. The results showed that the SOA reduced the errors by 5.1% and 13.6%, respectively, compared with a genetic algorithm (GA). Compared with particle swarm optimization (PSO), the error of SOA was reduced by 7.6% and 15.9%, respectively. It showed that SOA-SVM effectively improved the adaptability and regression performance of SVM, thus, significantly improving the prediction accuracy.https://www.mdpi.com/2075-1702/10/9/729support vector machinesquirrel algorithmrolling bearingremaining useful lifePCA analysis |
| spellingShingle | Xiao Li Songyang An Yuanyuan Shi Yizhe Huang Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm Machines support vector machine squirrel algorithm rolling bearing remaining useful life PCA analysis |
| title | Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm |
| title_full | Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm |
| title_fullStr | Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm |
| title_full_unstemmed | Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm |
| title_short | Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm |
| title_sort | remaining useful life estimation of rolling bearing based on soa svm algorithm |
| topic | support vector machine squirrel algorithm rolling bearing remaining useful life PCA analysis |
| url | https://www.mdpi.com/2075-1702/10/9/729 |
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