Application of recurrent neural network to mechanical fault diagnosis: a review
With the development of intelligent manufacturing and automation, the precision and complexity of mechanical equipment are increasing, which leads to a higher requirement for fault diagnosis. Fault diagnosis has gradually transformed from traditional diagnosis algorithm to deep feature mining and ex...
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| Published in | Journal of mechanical science and technology Vol. 36; no. 2; pp. 527 - 542 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Seoul
Korean Society of Mechanical Engineers
01.02.2022
Springer Nature B.V 대한기계학회 |
| Subjects | |
| Online Access | Get full text |
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| Abstract | With the development of intelligent manufacturing and automation, the precision and complexity of mechanical equipment are increasing, which leads to a higher requirement for fault diagnosis. Fault diagnosis has gradually transformed from traditional diagnosis algorithm to deep feature mining and expression of highly nonlinear, complex and multidimensional systems. At present, the mechanical fault signals of various equipment are mostly time series. In addition, recurrent neural network (RNN) has strong nonlinear feature learning and processing ability of time sequence information, which has achieved promising results in mechanical fault diagnosis and big data processing. Therefore, this study reviews state-of-the-art RNN method in mechanical fault diagnosis and introduces applications from two aspects: RNN and the combined neural networks which include RNN. Then, this paper discusses the challenges and future development of RNN based fault diagnosis. |
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| AbstractList | With the development of intelligent manufacturing and automation, the precision and complexity of mechanical equipment are increasing, which leads to a higher requirement for fault diagnosis. Fault diagnosis has gradually transformed from traditional diagnosis algorithm to deep feature mining and expression of highly nonlinear, complex and multidimensional systems. At present, the mechanical fault signals of various equipment are mostly time series. In addition, recurrent neural network (RNN) has strong nonlinear feature learning and processing ability of time sequence information, which has achieved promising results in mechanical fault diagnosis and big data processing. Therefore, this study reviews state-of-the-art RNN method in mechanical fault diagnosis and introduces applications from two aspects: RNN and the combined neural networks which include RNN. Then, this paper discusses the challenges and future development of RNN based fault diagnosis. With the development of intelligent manufacturing and automation, the precision and complexity of mechanical equipment are increasing, which leads to a higher requirement for fault diagnosis. Fault diagnosis has gradually transformed from traditional diagnosis algorithm to deep feature mining and expression of highly nonlinear, complex and multidimensional systems. At present, the mechanical fault signals of various equipment are mostly time series. In addition, recurrent neural network (RNN) has strong nonlinear feature learning and processing ability of time sequence information, which has achieved promising results in mechanical fault diagnosis and big data processing. Therefore, this study reviews state-of-the-art RNN method in mechanical fault diagnosis and introduces applications from two aspects: RNN and the combined neural networks which include RNN. Then, this paper discusses the challenges and future development of RNN based fault diagnosis. KCI Citation Count: 0 |
| Author | Shen, Yehu Qian, Chenhui Zhu, Junjun Xu, Fengyu Jiang, Quansheng Zhu, Qixin |
| Author_xml | – sequence: 1 givenname: Junjun surname: Zhu fullname: Zhu, Junjun organization: School of Mechanical Engineering, Suzhou University of Science and Technology – sequence: 2 givenname: Quansheng surname: Jiang fullname: Jiang, Quansheng email: qschiang@163.com organization: School of Mechanical Engineering, Suzhou University of Science and Technology – sequence: 3 givenname: Yehu surname: Shen fullname: Shen, Yehu organization: School of Mechanical Engineering, Suzhou University of Science and Technology – sequence: 4 givenname: Chenhui surname: Qian fullname: Qian, Chenhui organization: School of Mechanical Engineering, Suzhou University of Science and Technology – sequence: 5 givenname: Fengyu surname: Xu fullname: Xu, Fengyu organization: College of Automation, Nanjing University of Posts and Telecommunications – sequence: 6 givenname: Qixin surname: Zhu fullname: Zhu, Qixin organization: School of Mechanical Engineering, Suzhou University of Science and Technology |
| BackLink | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002810259$$DAccess content in National Research Foundation of Korea (NRF) |
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| Copyright | The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2022 The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2022. |
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| Keywords | Fult diagnosis Time series characteristics Recurrent neural network Structure optimization |
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| SubjectTerms | Algorithms Complexity Control Data processing Dynamical Systems Engineering Fault diagnosis Industrial and Production Engineering Intelligent manufacturing systems Machine learning Mechanical Engineering Neural networks Original Article Recurrent neural networks State-of-the-art reviews Vibration 기계공학 |
| Title | Application of recurrent neural network to mechanical fault diagnosis: a review |
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| ispartofPNX | Journal of Mechanical Science and Technology, 2022, 36(2), , pp.527-542 |
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