Data-driven fault identification method of RV reducer used in industrial robot
The RV reducers are complex and sealed mechanical systems that are difficult to conduct fault diagnosis in advance. The previous research worked on the fault identification of RV reducer were mainly carried out on the test platforms instead of real complex working conditions. Most of faults were int...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024161464 |
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| author | Dongdong Guo Yan Zhang Xiangqun Chen Hao Peng Zongrui Jiang Haitao Ma Wenbo Du |
| author_facet | Dongdong Guo Yan Zhang Xiangqun Chen Hao Peng Zongrui Jiang Haitao Ma Wenbo Du |
| author_sort | Dongdong Guo |
| collection | DOAJ |
| description | The RV reducers are complex and sealed mechanical systems that are difficult to conduct fault diagnosis in advance. The previous research worked on the fault identification of RV reducer were mainly carried out on the test platforms instead of real complex working conditions. Most of faults were intentionally created in laboratory instead of real malfunction caused by factory daily operation. In the present paper, the actual failure mode of RV reducer for the industrial robots in factory is taken as the goal of fault diagnosis. The constant speed segment data extraction method is designed to overcome the difficulty of frequency domain analysis caused by non-uniform rotation in working conditions and ensure the quality and effectiveness of features extraction. Several machine learning classification models are selected regarding their inherent features. The proper DNN binary classification model shows the best performance that can meet the requirements of fault identification in industrial environment. |
| format | Article |
| id | doaj-art-8aa17fc341f1450d8573260f2bd5f294 |
| institution | DOAJ |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-8aa17fc341f1450d8573260f2bd5f2942025-08-20T02:49:02ZengElsevierHeliyon2405-84402024-11-011022e4011510.1016/j.heliyon.2024.e40115Data-driven fault identification method of RV reducer used in industrial robotDongdong Guo0Yan Zhang1Xiangqun Chen2Hao Peng3Zongrui Jiang4Haitao Ma5Wenbo Du6Peking University School of Software and Microelectronics, 24 Jinyuan Road, Daxing Industrial District, Beijing, 102600, Beijing, China; Beijing Benz Automotive Co., Ltd., 8 Boxing Road, Beijing Economic-Technologlcal Development Area, Beijing, 100176, Beijing, ChinaBeijing Benz Automotive Co., Ltd., 8 Boxing Road, Beijing Economic-Technologlcal Development Area, Beijing, 100176, Beijing, China; Corresponding author.Peking University School of Software and Microelectronics, 24 Jinyuan Road, Daxing Industrial District, Beijing, 102600, Beijing, ChinaBeijing Benz Automotive Co., Ltd., 8 Boxing Road, Beijing Economic-Technologlcal Development Area, Beijing, 100176, Beijing, ChinaBeijing Benz Automotive Co., Ltd., 8 Boxing Road, Beijing Economic-Technologlcal Development Area, Beijing, 100176, Beijing, ChinaBeijing Benz Automotive Co., Ltd., 8 Boxing Road, Beijing Economic-Technologlcal Development Area, Beijing, 100176, Beijing, ChinaBeijing Benz Automotive Co., Ltd., 8 Boxing Road, Beijing Economic-Technologlcal Development Area, Beijing, 100176, Beijing, ChinaThe RV reducers are complex and sealed mechanical systems that are difficult to conduct fault diagnosis in advance. The previous research worked on the fault identification of RV reducer were mainly carried out on the test platforms instead of real complex working conditions. Most of faults were intentionally created in laboratory instead of real malfunction caused by factory daily operation. In the present paper, the actual failure mode of RV reducer for the industrial robots in factory is taken as the goal of fault diagnosis. The constant speed segment data extraction method is designed to overcome the difficulty of frequency domain analysis caused by non-uniform rotation in working conditions and ensure the quality and effectiveness of features extraction. Several machine learning classification models are selected regarding their inherent features. The proper DNN binary classification model shows the best performance that can meet the requirements of fault identification in industrial environment.http://www.sciencedirect.com/science/article/pii/S2405844024161464RV reducerIndustrial robotVibration signal processingMachine learningFault identification |
| spellingShingle | Dongdong Guo Yan Zhang Xiangqun Chen Hao Peng Zongrui Jiang Haitao Ma Wenbo Du Data-driven fault identification method of RV reducer used in industrial robot Heliyon RV reducer Industrial robot Vibration signal processing Machine learning Fault identification |
| title | Data-driven fault identification method of RV reducer used in industrial robot |
| title_full | Data-driven fault identification method of RV reducer used in industrial robot |
| title_fullStr | Data-driven fault identification method of RV reducer used in industrial robot |
| title_full_unstemmed | Data-driven fault identification method of RV reducer used in industrial robot |
| title_short | Data-driven fault identification method of RV reducer used in industrial robot |
| title_sort | data driven fault identification method of rv reducer used in industrial robot |
| topic | RV reducer Industrial robot Vibration signal processing Machine learning Fault identification |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024161464 |
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