RFR-GA-BLS: A Feature Selection and Parameter Optimization Method for Fault Diagnosis of Rolling Bearing Using Infrared Images
To overcome the problems of low machine learning fault diagnosis rate and long consumption time of deep learning in rolling bearing fault diagnosis, an RFR-GA-BLS model is proposed. The model is validated by infrared images of rolling bearings to find the most representative features, the most suita...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/13/7350 |
| _version_ | 1797592234082697216 |
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| author | Jianmin Zhou Lulu Liu Xiwen Shen Xiaotong Yang |
| author_facet | Jianmin Zhou Lulu Liu Xiwen Shen Xiaotong Yang |
| author_sort | Jianmin Zhou |
| collection | DOAJ |
| description | To overcome the problems of low machine learning fault diagnosis rate and long consumption time of deep learning in rolling bearing fault diagnosis, an RFR-GA-BLS model is proposed. The model is validated by infrared images of rolling bearings to find the most representative features, the most suitable parameters and the best diagnostic rate. Based on the pre-processed infrared thermal images of the faulty bearing, 72 second-order statistical features were obtained as information for fault diagnosis. RFR considered the robustness of the features, and new sequences were obtained. BLS was optimized by GA for fault diagnosis. New sequence features were added to the model sequentially, one at a time. After satisfying the model conditions, the most appropriate number of features was selected as the first 20. The search results for the number of feature nodes, the number of feature node windows and the number of enhancement nodes for the BLS were 24, 19 and 544, respectively, and the fault diagnosis rate of 98.8889% was achieved. According to a comparison with CFR-GA-BLS, BLS, PSO-BLS and Grdy-BLS, our proposed model is more advantageous in the search for the best performance. The fault diagnosis accuracy is higher compared to SVM and RF. The speed of our proposed model is 207 times faster than 1DCNN and 10,147 times faster than 2DCNN. |
| first_indexed | 2024-03-11T01:48:33Z |
| format | Article |
| id | doaj.art-e77a1f13549a48a0a62c6c02a861c274 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:48:33Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e77a1f13549a48a0a62c6c02a861c2742023-11-18T16:05:08ZengMDPI AGApplied Sciences2076-34172023-06-011313735010.3390/app13137350RFR-GA-BLS: A Feature Selection and Parameter Optimization Method for Fault Diagnosis of Rolling Bearing Using Infrared ImagesJianmin Zhou0Lulu Liu1Xiwen Shen2Xiaotong Yang3Key Laboratory of Conveyance and Equipment, East China Jiaotong University, Ministry of Education, Nanchang 330013, ChinaKey Laboratory of Conveyance and Equipment, East China Jiaotong University, Ministry of Education, Nanchang 330013, ChinaKey Laboratory of Conveyance and Equipment, East China Jiaotong University, Ministry of Education, Nanchang 330013, ChinaKey Laboratory of Conveyance and Equipment, East China Jiaotong University, Ministry of Education, Nanchang 330013, ChinaTo overcome the problems of low machine learning fault diagnosis rate and long consumption time of deep learning in rolling bearing fault diagnosis, an RFR-GA-BLS model is proposed. The model is validated by infrared images of rolling bearings to find the most representative features, the most suitable parameters and the best diagnostic rate. Based on the pre-processed infrared thermal images of the faulty bearing, 72 second-order statistical features were obtained as information for fault diagnosis. RFR considered the robustness of the features, and new sequences were obtained. BLS was optimized by GA for fault diagnosis. New sequence features were added to the model sequentially, one at a time. After satisfying the model conditions, the most appropriate number of features was selected as the first 20. The search results for the number of feature nodes, the number of feature node windows and the number of enhancement nodes for the BLS were 24, 19 and 544, respectively, and the fault diagnosis rate of 98.8889% was achieved. According to a comparison with CFR-GA-BLS, BLS, PSO-BLS and Grdy-BLS, our proposed model is more advantageous in the search for the best performance. The fault diagnosis accuracy is higher compared to SVM and RF. The speed of our proposed model is 207 times faster than 1DCNN and 10,147 times faster than 2DCNN.https://www.mdpi.com/2076-3417/13/13/7350RFRGA-BLSparameter searchoptimal characteristicsfault diagnosisrolling bearings |
| spellingShingle | Jianmin Zhou Lulu Liu Xiwen Shen Xiaotong Yang RFR-GA-BLS: A Feature Selection and Parameter Optimization Method for Fault Diagnosis of Rolling Bearing Using Infrared Images Applied Sciences RFR GA-BLS parameter search optimal characteristics fault diagnosis rolling bearings |
| title | RFR-GA-BLS: A Feature Selection and Parameter Optimization Method for Fault Diagnosis of Rolling Bearing Using Infrared Images |
| title_full | RFR-GA-BLS: A Feature Selection and Parameter Optimization Method for Fault Diagnosis of Rolling Bearing Using Infrared Images |
| title_fullStr | RFR-GA-BLS: A Feature Selection and Parameter Optimization Method for Fault Diagnosis of Rolling Bearing Using Infrared Images |
| title_full_unstemmed | RFR-GA-BLS: A Feature Selection and Parameter Optimization Method for Fault Diagnosis of Rolling Bearing Using Infrared Images |
| title_short | RFR-GA-BLS: A Feature Selection and Parameter Optimization Method for Fault Diagnosis of Rolling Bearing Using Infrared Images |
| title_sort | rfr ga bls a feature selection and parameter optimization method for fault diagnosis of rolling bearing using infrared images |
| topic | RFR GA-BLS parameter search optimal characteristics fault diagnosis rolling bearings |
| url | https://www.mdpi.com/2076-3417/13/13/7350 |
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