Fuzzy Broad Learning System Combined with Feature-Engineering-Based Fault Diagnosis for Bearings
Bearings are essential components of rotating machinery used in mechanical systems, and fault diagnosis of bearings is of great significance to the operation and maintenance of mechanical equipment. Deep learning is a popular method for bearing fault diagnosis, which can effectively extract the in-d...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/10/12/1229 |
| _version_ | 1797456624860790784 |
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| author | Jianmin Zhou Xiaotong Yang Lulu Liu Yunqing Wang Junjie Wang Guanghao Hou |
| author_facet | Jianmin Zhou Xiaotong Yang Lulu Liu Yunqing Wang Junjie Wang Guanghao Hou |
| author_sort | Jianmin Zhou |
| collection | DOAJ |
| description | Bearings are essential components of rotating machinery used in mechanical systems, and fault diagnosis of bearings is of great significance to the operation and maintenance of mechanical equipment. Deep learning is a popular method for bearing fault diagnosis, which can effectively extract the in-depth information of fault signals, thus achieving high fault diagnosis accuracy. However, due to the complex deep structure of deep learning, most deep learning methods require more time and resources for bearing fault diagnosis. This paper proposes a bearing fault diagnosis method combining feature engineering and fuzzy broad learning. First, time domain, frequency domain, and time-frequency domain features are extracted from the bearing signals. Then the stability and robustness indexes of these features are evaluated to complete the feature engineering. The features obtained by feature engineering are used as the input of the fault diagnosis model, and three sets of experimental data validate the model. The experimental results show that the proposed method can achieve the bearing fault diagnosis accuracy of 96.43% on the experimental bench data, 100% on the Case Western Reserve University dataset, and 100% on the centrifugal pump bearing fault dataset, with a time of approximately 0.28 s. The results show that this method has the advantages of accuracy, rapidity, and stability of bearing fault diagnosis. |
| first_indexed | 2024-03-09T16:10:30Z |
| format | Article |
| id | doaj.art-e98b3c13fd6b471ab587faa92352f24a |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T16:10:30Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-e98b3c13fd6b471ab587faa92352f24a2023-11-24T16:17:49ZengMDPI AGMachines2075-17022022-12-011012122910.3390/machines10121229Fuzzy Broad Learning System Combined with Feature-Engineering-Based Fault Diagnosis for BearingsJianmin Zhou0Xiaotong Yang1Lulu Liu2Yunqing Wang3Junjie Wang4Guanghao Hou5Key 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, 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, ChinaBearings are essential components of rotating machinery used in mechanical systems, and fault diagnosis of bearings is of great significance to the operation and maintenance of mechanical equipment. Deep learning is a popular method for bearing fault diagnosis, which can effectively extract the in-depth information of fault signals, thus achieving high fault diagnosis accuracy. However, due to the complex deep structure of deep learning, most deep learning methods require more time and resources for bearing fault diagnosis. This paper proposes a bearing fault diagnosis method combining feature engineering and fuzzy broad learning. First, time domain, frequency domain, and time-frequency domain features are extracted from the bearing signals. Then the stability and robustness indexes of these features are evaluated to complete the feature engineering. The features obtained by feature engineering are used as the input of the fault diagnosis model, and three sets of experimental data validate the model. The experimental results show that the proposed method can achieve the bearing fault diagnosis accuracy of 96.43% on the experimental bench data, 100% on the Case Western Reserve University dataset, and 100% on the centrifugal pump bearing fault dataset, with a time of approximately 0.28 s. The results show that this method has the advantages of accuracy, rapidity, and stability of bearing fault diagnosis.https://www.mdpi.com/2075-1702/10/12/1229bearing fault diagnosisfuzzy broad learning systemfeature engineeringbearingfuzzy rules |
| spellingShingle | Jianmin Zhou Xiaotong Yang Lulu Liu Yunqing Wang Junjie Wang Guanghao Hou Fuzzy Broad Learning System Combined with Feature-Engineering-Based Fault Diagnosis for Bearings Machines bearing fault diagnosis fuzzy broad learning system feature engineering bearing fuzzy rules |
| title | Fuzzy Broad Learning System Combined with Feature-Engineering-Based Fault Diagnosis for Bearings |
| title_full | Fuzzy Broad Learning System Combined with Feature-Engineering-Based Fault Diagnosis for Bearings |
| title_fullStr | Fuzzy Broad Learning System Combined with Feature-Engineering-Based Fault Diagnosis for Bearings |
| title_full_unstemmed | Fuzzy Broad Learning System Combined with Feature-Engineering-Based Fault Diagnosis for Bearings |
| title_short | Fuzzy Broad Learning System Combined with Feature-Engineering-Based Fault Diagnosis for Bearings |
| title_sort | fuzzy broad learning system combined with feature engineering based fault diagnosis for bearings |
| topic | bearing fault diagnosis fuzzy broad learning system feature engineering bearing fuzzy rules |
| url | https://www.mdpi.com/2075-1702/10/12/1229 |
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