SCG-GFFE: A Self-Constructed graph fault feature extractor based on graph Auto-encoder algorithm for unlabeled single-variable vibration signals of harmonic reducer
As a pivotal component in robotic systems, harmonic reducer fault diagnosis plays a crucial role in safe and stable operation; however, the lack of labelled fault samples hampers its effectiveness. This study introduces a self-constructed graph graph-autoencoder fault feature extractor (SCG-GFFE), a...
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| Published in | Advanced engineering informatics Vol. 62; p. 102579 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.10.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1474-0346 |
| DOI | 10.1016/j.aei.2024.102579 |
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| Abstract | As a pivotal component in robotic systems, harmonic reducer fault diagnosis plays a crucial role in safe and stable operation; however, the lack of labelled fault samples hampers its effectiveness. This study introduces a self-constructed graph graph-autoencoder fault feature extractor (SCG-GFFE), a novel method that uses the Graph Auto-encoder (GAE) algorithm. SCG-GFFE leverages Graph Neural Networks (GNNs) for the unsupervised extraction of fault features, enhancing fault diagnosis in scenarios with limited labelled data. This approach overcomes the challenges of graph construction for fault diagnosis, particularly for single-sensor vibration signals. First, we developed the SCG-GFFE method for efficient fault feature extraction, which does not require complex domain expertise and is capable of generating graph structures. Second, we designed a suitably structured subgraph for dealing with single, unlabelled, non-multisource vibration signals. Third, a real-world harmonic reducer’s vibrational signal is utilised to build an experimental study and demonstrate the effectiveness of the SCG-GFFE method. The results of SCG-GFFE demonstrate remarkable accuracy in fault diagnosis, consistently exceeding 98% across various classifiers, and indicate that the proposed method can solve the problem of limited labelled data in harmonic reducer diagnosis. |
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| AbstractList | As a pivotal component in robotic systems, harmonic reducer fault diagnosis plays a crucial role in safe and stable operation; however, the lack of labelled fault samples hampers its effectiveness. This study introduces a self-constructed graph graph-autoencoder fault feature extractor (SCG-GFFE), a novel method that uses the Graph Auto-encoder (GAE) algorithm. SCG-GFFE leverages Graph Neural Networks (GNNs) for the unsupervised extraction of fault features, enhancing fault diagnosis in scenarios with limited labelled data. This approach overcomes the challenges of graph construction for fault diagnosis, particularly for single-sensor vibration signals. First, we developed the SCG-GFFE method for efficient fault feature extraction, which does not require complex domain expertise and is capable of generating graph structures. Second, we designed a suitably structured subgraph for dealing with single, unlabelled, non-multisource vibration signals. Third, a real-world harmonic reducer’s vibrational signal is utilised to build an experimental study and demonstrate the effectiveness of the SCG-GFFE method. The results of SCG-GFFE demonstrate remarkable accuracy in fault diagnosis, consistently exceeding 98% across various classifiers, and indicate that the proposed method can solve the problem of limited labelled data in harmonic reducer diagnosis. |
| ArticleNumber | 102579 |
| Author | Xu, Wenfu Ding, Hao Zhao, Zida Wang, Dong Sun, Shilong |
| Author_xml | – sequence: 1 givenname: Shilong orcidid: 0000-0003-0460-4592 surname: Sun fullname: Sun, Shilong email: sslmy526@gmail.com, sunshilong@hit.edu.cn, shilosun-c@my.cityu.edu.hk organization: School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China – sequence: 2 givenname: Hao surname: Ding fullname: Ding, Hao organization: School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China – sequence: 3 givenname: Zida surname: Zhao fullname: Zhao, Zida organization: School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China – sequence: 4 givenname: Wenfu surname: Xu fullname: Xu, Wenfu organization: School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China – sequence: 5 givenname: Dong surname: Wang fullname: Wang, Dong organization: The State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China |
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| Keywords | Graph autoencoder Self-constructed graph Unsupervised fault feature extractor Single-variable vibration signals |
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| SubjectTerms | Graph autoencoder Self-constructed graph Single-variable vibration signals Unsupervised fault feature extractor |
| Title | SCG-GFFE: A Self-Constructed graph fault feature extractor based on graph Auto-encoder algorithm for unlabeled single-variable vibration signals of harmonic reducer |
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