Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader
Incipient fault detection and identification (IFDI) of cutting arms is a crucial guarantee for the smooth operation of a roadheader. However, the shortage of fault samples restricts the application of the fault diagnosis technique, and the data analysis tools should be optimized efficiently. In this...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/8865068 |
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| _version_ | 1832552525320945664 |
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| author | Qiang Liu Songyong Liu Qianjin Dai Xiao Yu Daoxiang Teng Ming Wei |
| author_facet | Qiang Liu Songyong Liu Qianjin Dai Xiao Yu Daoxiang Teng Ming Wei |
| author_sort | Qiang Liu |
| collection | DOAJ |
| description | Incipient fault detection and identification (IFDI) of cutting arms is a crucial guarantee for the smooth operation of a roadheader. However, the shortage of fault samples restricts the application of the fault diagnosis technique, and the data analysis tools should be optimized efficiently. In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. The commonly measured current and vibration data cutting arms are used in the IFDI. The experimental results show that the support vector machines based on dynamic cuckoo outperform the other methods. Besides, the performance of the four methods under different operating conditions is compared. The fault cause of cutting arms of the roadheader is analyzed and the design improvement scheme for cutting arms is provided. This study provides a reference for improving the fault diagnosis of the roadheader. |
| format | Article |
| id | doaj-art-bc1adadc8db645f2ad387bb7381ff2d6 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-bc1adadc8db645f2ad387bb7381ff2d62025-02-03T05:58:30ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/88650688865068Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the RoadheaderQiang Liu0Songyong Liu1Qianjin Dai2Xiao Yu3Daoxiang Teng4Ming Wei5School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Physics and New Energy, Xuzhou University of Technology, Xuzhou 221018, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Physics and New Energy, Xuzhou University of Technology, Xuzhou 221018, ChinaSchool of Physics and New Energy, Xuzhou University of Technology, Xuzhou 221018, ChinaIncipient fault detection and identification (IFDI) of cutting arms is a crucial guarantee for the smooth operation of a roadheader. However, the shortage of fault samples restricts the application of the fault diagnosis technique, and the data analysis tools should be optimized efficiently. In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. The commonly measured current and vibration data cutting arms are used in the IFDI. The experimental results show that the support vector machines based on dynamic cuckoo outperform the other methods. Besides, the performance of the four methods under different operating conditions is compared. The fault cause of cutting arms of the roadheader is analyzed and the design improvement scheme for cutting arms is provided. This study provides a reference for improving the fault diagnosis of the roadheader.http://dx.doi.org/10.1155/2021/8865068 |
| spellingShingle | Qiang Liu Songyong Liu Qianjin Dai Xiao Yu Daoxiang Teng Ming Wei Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader Shock and Vibration |
| title | Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader |
| title_full | Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader |
| title_fullStr | Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader |
| title_full_unstemmed | Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader |
| title_short | Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader |
| title_sort | data driven approaches for diagnosis of incipient faults in cutting arms of the roadheader |
| url | http://dx.doi.org/10.1155/2021/8865068 |
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