A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis
Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time co...
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| Published in: | Frontiers of Mechanical Engineering Vol. 17; no. 2; p. 17 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Beijing
Higher Education Press
01.06.2022
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| ISSN: | 2095-0233, 2095-0241 |
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| Abstract | Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis. |
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| AbstractList | Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis. |
| ArticleNumber | 17 |
| Author | WANG, You WEN, Long LI, Xinyu |
| Author_xml | – sequence: 1 givenname: Long surname: WEN fullname: WEN, Long organization: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China – sequence: 2 givenname: You surname: WANG fullname: WANG, You organization: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China – sequence: 3 givenname: Xinyu surname: LI fullname: LI, Xinyu email: Xinyu LI organization: State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China |
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| Cites_doi | 10.1109/TIE.2018.2844805 10.1109/4235.585893 10.3390/s20144017 10.3390/s17020425 10.1007/978-1-4471-0123-9_3 10.1016/j.ymssp.2021.107773 10.1007/s42835-020-00343-7 10.1016/j.measurement.2020.108122 10.1109/TII.2019.2950667 10.1016/j.ymssp.2020.106683 10.1109/TSM.2020.3020985 10.1016/j.measurement.2019.107417 10.1109/TIM.2019.2902003 10.1109/TCYB.2019.2939174 10.1007/s11465-018-0472-3 10.1016/j.engappai.2020.103966 10.3390/s20041233 10.1016/j.neucom.2018.09.050 10.1016/j.neucom.2019.11.006 10.1109/TII.2020.3044106 10.1109/TII.2019.2938884 10.1007/978-3-030-05318-5 10.1109/ACCESS.2019.2936625 10.1109/TIE.2020.3044808 10.1007/s11465-017-0443-0 10.1007/s10462-020-09910-w 10.1177/0020294020932347 10.1016/j.cogsys.2018.03.002 10.1109/TIM.2019.2896370 10.3390/app10103659 10.1016/j.ymssp.2019.106587 10.1007/s11465-021-0629-3 10.1016/j.neucom.2020.07.088 |
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| Keywords | fault diagnosis deep reinforcement learning hyper parameter optimization convolutional neural network |
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| Notes | Document accepted on :2022-01-10 fault diagnosis Document received on :2021-09-03 deep reinforcement learning hyper parameter optimization convolutional neural network |
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| PublicationTitle | Frontiers of Mechanical Engineering |
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| References | Li, Jamieson, DeSalvo, Rostamizadeh, Talwalkar (CR27) 2018; 18 Wolpert, Macready (CR7) 1997; 1 Xu, Liu, Jiang, Shen, Huang (CR17) 2020; 69 Wei, Huang, Yao, Hu, Fan, Huang (CR31) 2020; 96 Wang, Xu, Dai, Zhang, Zhong (CR5) 2021; 17 CR14 Zhang, Peng, Li, Chen, Zhang (CR37) 2017; 17 Wolpert, Roy, Köppen, Ovaska, Furuhashi, Hoffmann (CR8) 2002 CR13 CR12 Chen, Huang, Zhao, Wang, Liu, Li (CR19) 2021; 70 Li, Wang, Song, Wang, Cui, Lin (CR22) 2020; 165 Hutter, Kotthoff, Vanschoren (CR9) 2019 CR32 Kolar, Lisjak, Pająk, Pavković (CR23) 2020; 20 Wang, Xu, Yang, Zhang, Li (CR16) 2020; 33 Jiao, Zhao, Lin, Liang (CR20) 2020; 417 Wang, Ning, Feng (CR35) 2020; 10 Qiao, Wang, Wang, Zhang, Xu (CR40) 2019; 7 Wen, Li, Gao (CR36) 2020; 69 Li, Zhang, Qin, Sun (CR28) 2020; 234 Wen, Ye, Gao (CR11) 2020; 53 Li, Zheng, Wang, Cao, Guo, Fu (CR18) 2021; 70 Zhang, Chen, Chen, Li (CR33) 2021; 51 Cabrera, Guamán, Zhang, Cerrada, Sánchez, Cevallos, Long, Li (CR26) 2020; 380 Hoang, Kang (CR38) 2019; 53 Chen, Mauricio, Li, Gryllias (CR6) 2020; 140 Wang, Jiang, Li, Liu (CR24) 2020; 154 Zhu, Peng, Chen, Gao (CR34) 2019; 323 Wen, Li, Gao (CR10) 2021; 68 Chen, Wang, Qiao, Chen (CR2) 2018; 13 Nath, Udmale, Singh (CR4) 2021; 54 Song, Li, Jia, Qiu (CR41) 2020; 16 Yao, Zhang, Yang, Gui (CR21) 2020; 20 Jiang, He, Yan, Xie (CR39) 2019; 66 Zhou, Peng, Chen, Yang, Zhang (CR15) 2018; 13 Han, Choi, Park, Hong (CR30) 2020; 15 Long, Zhang, Li (CR29) 2020; 16 Zhang, Huang, Wu, Hu, Huang, Zhou, Zhang (CR1) 2021; 16 Lei, Yang, Jiang, Jia, Li, Nandi (CR3) 2020; 138 Zhang, Chen, He, Xu, Li, Zhou (CR25) 2021; 158 Y G Lei (673_CR3) 2020; 138 Y Yao (673_CR21) 2020; 20 J H Han (673_CR30) 2020; 15 J Y Long (673_CR29) 2020; 16 L Wen (673_CR36) 2020; 69 D H Wolpert (673_CR8) 2002 Y Song (673_CR41) 2020; 16 X F Chen (673_CR2) 2018; 13 673_CR14 673_CR13 P Zhou (673_CR15) 2018; 13 673_CR12 L Wen (673_CR11) 2020; 53 J B Chen (673_CR19) 2021; 70 673_CR32 Z X Li (673_CR18) 2021; 70 D H Wolpert (673_CR7) 1997; 1 Z Y Chen (673_CR6) 2020; 140 R X Wang (673_CR24) 2020; 154 L Wen (673_CR10) 2021; 68 H Li (673_CR28) 2020; 234 G Q Jiang (673_CR39) 2019; 66 S Li (673_CR22) 2020; 165 F Hutter (673_CR9) 2019 K Y Zhang (673_CR25) 2021; 158 J A Wei (673_CR31) 2020; 96 Y Wang (673_CR35) 2020; 10 Z Z Zhang (673_CR33) 2021; 51 W Zhang (673_CR37) 2017; 17 J L Wang (673_CR5) 2021; 17 X Zhang (673_CR1) 2021; 16 G W Xu (673_CR17) 2020; 69 L Li (673_CR27) 2018; 18 Z Y Zhu (673_CR34) 2019; 323 A G Nath (673_CR4) 2021; 54 H H Qiao (673_CR40) 2019; 7 J L Wang (673_CR16) 2020; 33 J Y Jiao (673_CR20) 2020; 417 D Kolar (673_CR23) 2020; 20 D T Hoang (673_CR38) 2019; 53 D Cabrera (673_CR26) 2020; 380 |
| References_xml | – volume: 66 start-page: 3196 issue: 4 year: 2019 end-page: 3207 ident: CR39 article-title: Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox publication-title: IEEE Transactions on Industrial Electronics doi: 10.1109/TIE.2018.2844805 – volume: 234 start-page: 343 issue: 1 year: 2020 end-page: 360 ident: CR28 article-title: Raw vibration signal pattern recognition with automatic hyper-parameter-optimized convolutional neural network for bearing fault diagnosis publication-title: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science – volume: 1 start-page: 67 issue: 1 year: 1997 end-page: 82 ident: CR7 article-title: No free lunch theorems for optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.585893 – volume: 20 start-page: 4017 issue: 14 year: 2020 ident: CR23 article-title: Fault diagnosis of rotary machines using deep convolutional neural network with wide three axis vibration signal input publication-title: Sensors doi: 10.3390/s20144017 – ident: CR14 – volume: 17 start-page: 425 issue: 2 year: 2017 ident: CR37 article-title: A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals publication-title: Sensors doi: 10.3390/s17020425 – start-page: 25 year: 2002 end-page: 42 ident: CR8 article-title: The supervised learning no-free-lunch theorems publication-title: Soft Computing and Industry doi: 10.1007/978-1-4471-0123-9_3 – volume: 158 start-page: 107773 year: 2021 ident: CR25 article-title: Differentiable neural architecture search augmented with pruning and multi-objective optimization for time-efficient intelligent fault diagnosis of machinery publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2021.107773 – volume: 18 start-page: 6765 issue: 1 year: 2018 end-page: 6816 ident: CR27 article-title: Hyperband: a novel bandit-based approach to hyperparameter optimization publication-title: The Journal of Machine Learning Research – ident: CR12 – volume: 15 start-page: 721 issue: 2 year: 2020 end-page: 726 ident: CR30 article-title: Hyperparameter optimization using a genetic algorithm considering verification time in a convolutional neural network publication-title: Journal of Electrical Engineering & Technology doi: 10.1007/s42835-020-00343-7 – volume: 165 start-page: 108122 year: 2020 ident: CR22 article-title: An adaptive data fusion strategy for fault diagnosis based on the convolutional neural network publication-title: Measurement doi: 10.1016/j.measurement.2020.108122 – volume: 16 start-page: 6163 issue: 9 year: 2020 end-page: 6171 ident: CR41 article-title: Retraining strategy-based domain adaption network for intelligent fault diagnosis publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2019.2950667 – volume: 140 start-page: 106683 year: 2020 ident: CR6 article-title: A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2020.106683 – volume: 33 start-page: 587 issue: 4 year: 2020 end-page: 596 ident: CR16 article-title: Deformable convolutional networks for efficient mixed-type wafer defect pattern recognition publication-title: IEEE Transactions on Semiconductor Manufacturing doi: 10.1109/TSM.2020.3020985 – volume: 154 start-page: 107417 year: 2020 ident: CR24 article-title: A reinforcement neural architecture search method for rolling bearing fault diagnosis publication-title: Measurement doi: 10.1016/j.measurement.2019.107417 – volume: 69 start-page: 509 issue: 2 year: 2020 end-page: 520 ident: CR17 article-title: Online fault diagnosis method based on transfer convolutional neural networks publication-title: IEEE Transactions on Instrumentation and Measurement doi: 10.1109/TIM.2019.2902003 – volume: 70 start-page: 3500417 year: 2021 ident: CR18 article-title: A novel method for imbalanced fault diagnosis of rotating machinery based on generative adversarial networks publication-title: IEEE Transactions on Instrumentation and Measurement – volume: 51 start-page: 604 issue: 2 year: 2021 end-page: 613 ident: CR33 article-title: Asynchronous episodic deep deterministic policy gradient: toward continuous control in computationally complex environments publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2019.2939174 – volume: 13 start-page: 264 issue: 2 year: 2018 end-page: 291 ident: CR2 article-title: Basic research on machinery fault diagnostics: past, present, and future trends publication-title: Frontiers of Mechanical Engineering doi: 10.1007/s11465-018-0472-3 – volume: 96 start-page: 103966 year: 2020 ident: CR31 article-title: New imbalanced fault diagnosis framework based on cluster-MWMOTE and MFO-optimized LS-SVM using limited and complex bearing data publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2020.103966 – volume: 20 start-page: 1233 issue: 4 year: 2020 ident: CR21 article-title: Learning attention representation with a multi-scale CNN for gear fault diagnosis under different working conditions publication-title: Sensors doi: 10.3390/s20041233 – volume: 323 start-page: 62 year: 2019 end-page: 75 ident: CR34 article-title: A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.09.050 – volume: 380 start-page: 51 year: 2020 end-page: 66 ident: CR26 article-title: Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.11.006 – volume: 17 start-page: 7913 issue: 12 year: 2021 end-page: 7922 ident: CR5 article-title: An unequal deep learning approach for 3-D point cloud segmentation publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2020.3044106 – volume: 16 start-page: 4928 issue: 7 year: 2020 end-page: 4937 ident: CR29 article-title: Evolving deep echo state networks for intelligent fault diagnosis publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2019.2938884 – year: 2019 ident: CR9 publication-title: Automated Machine Learning: Methods, Systems, Challenges doi: 10.1007/978-3-030-05318-5 – volume: 7 start-page: 118954 year: 2019 end-page: 118964 ident: CR40 article-title: An adaptive weighted multiscale convolutional neural network for rotating machinery fault diagnosis under variable operating conditions publication-title: IEEE Access: Practical Innovations, Open Solutions doi: 10.1109/ACCESS.2019.2936625 – ident: CR13 – volume: 68 start-page: 12890 issue: 12 year: 2021 end-page: 12900 ident: CR10 article-title: A new reinforcement learning based learning rate scheduler for convolutional neural network in fault classification publication-title: IEEE Transactions on Industrial Electronics doi: 10.1109/TIE.2020.3044808 – volume: 13 start-page: 292 issue: 2 year: 2018 end-page: 300 ident: CR15 article-title: Non-stationary signal analysis based on general parameterized time-frequency transform and its application in the feature extraction of a rotary machine publication-title: Frontiers of Mechanical Engineering doi: 10.1007/s11465-017-0443-0 – ident: CR32 – volume: 54 start-page: 2609 year: 2021 end-page: 2668 ident: CR4 article-title: Role of artificial intelligence in rotor fault diagnosis: a comprehensive review publication-title: Artificial Intelligence Review doi: 10.1007/s10462-020-09910-w – volume: 53 start-page: 1088 issue: 7–8 year: 2020 end-page: 1098 ident: CR11 article-title: A new automatic machine learning based hyperparameter optimization for workpiece quality prediction publication-title: Measurement and Control doi: 10.1177/0020294020932347 – volume: 53 start-page: 42 year: 2019 end-page: 50 ident: CR38 article-title: Rolling element bearing fault diagnosis using convolutional neural network and vibration image publication-title: Cognitive Systems Research doi: 10.1016/j.cogsys.2018.03.002 – volume: 69 start-page: 330 issue: 2 year: 2020 end-page: 338 ident: CR36 article-title: A new two-level hierarchical diagnosis network based on convolutional neural network publication-title: IEEE Transactions on Instrumentation and Measurement doi: 10.1109/TIM.2019.2896370 – volume: 10 start-page: 3659 issue: 10 year: 2020 ident: CR35 article-title: A novel capsule network based on wide convolution and multi-scale convolution for fault diagnosis publication-title: Applied Sciences doi: 10.3390/app10103659 – volume: 138 start-page: 106587 year: 2020 ident: CR3 article-title: Applications of machine learning to machine fault diagnosis: a review and roadmap publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2019.106587 – volume: 16 start-page: 340 issue: 2 year: 2021 end-page: 352 ident: CR1 article-title: Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples publication-title: Frontiers of Mechanical Engineering doi: 10.1007/s11465-021-0629-3 – volume: 417 start-page: 36 year: 2020 end-page: 63 ident: CR20 article-title: A comprehensive review on convolutional neural network in machine fault diagnosis publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.07.088 – volume: 70 start-page: 3517010 year: 2021 ident: CR19 article-title: Multiscale convolutional neural network with feature alignment for bearing fault diagnosis publication-title: IEEE Transactions on Instrumentation and Measurement – volume: 138 start-page: 106587 year: 2020 ident: 673_CR3 publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2019.106587 – 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673_CR6 publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2020.106683 – ident: 673_CR12 – volume: 16 start-page: 6163 issue: 9 year: 2020 ident: 673_CR41 publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2019.2950667 – volume: 17 start-page: 7913 issue: 12 year: 2021 ident: 673_CR5 publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2020.3044106 – volume: 16 start-page: 4928 issue: 7 year: 2020 ident: 673_CR29 publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2019.2938884 – volume: 417 start-page: 36 year: 2020 ident: 673_CR20 publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.07.088 – volume: 53 start-page: 1088 issue: 7–8 year: 2020 ident: 673_CR11 publication-title: Measurement and Control doi: 10.1177/0020294020932347 – volume: 96 start-page: 103966 year: 2020 ident: 673_CR31 publication-title: Engineering Applications of Artificial Intelligence doi: 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10.1016/j.measurement.2019.107417 – volume: 18 start-page: 6765 issue: 1 year: 2018 ident: 673_CR27 publication-title: The Journal of Machine Learning Research – volume-title: Automated Machine Learning: Methods, Systems, Challenges year: 2019 ident: 673_CR9 doi: 10.1007/978-3-030-05318-5 – volume: 158 start-page: 107773 year: 2021 ident: 673_CR25 publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2021.107773 – ident: 673_CR13 – volume: 13 start-page: 292 issue: 2 year: 2018 ident: 673_CR15 publication-title: Frontiers of Mechanical Engineering doi: 10.1007/s11465-017-0443-0 – volume: 69 start-page: 330 issue: 2 year: 2020 ident: 673_CR36 publication-title: IEEE Transactions on Instrumentation and Measurement doi: 10.1109/TIM.2019.2896370 – volume: 15 start-page: 721 issue: 2 year: 2020 ident: 673_CR30 publication-title: Journal of Electrical Engineering & Technology doi: 10.1007/s42835-020-00343-7 – volume: 234 start-page: 343 issue: 1 year: 2020 ident: 673_CR28 publication-title: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science – volume: 1 start-page: 67 issue: 1 year: 1997 ident: 673_CR7 publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.585893 |
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| Snippet | Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model... |
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| SubjectTerms | convolutional neural network deep reinforcement learning Engineering fault diagnosis hyper parameter optimization Intelligent Diagnosis and Maintenance Mechanical Engineering Research Article |
| Title | A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis |
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