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| Citation: |
YU K W, LI Z R, CHEN C, et al. Deep residual shrinkage adaptive network-based cloud-edge-end collaborative fault diagnosis method for propulsion shafting system[J]. Chinese Journal of Ship Research, 2025, 20(2): 1–9 (in Chinese). DOI: 10.19693/j.issn.1673-3185.03779
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Aiming at problems including the fact that the fault diagnosis model of propulsion shafting systems under variable working conditions has poor generalization and cannot learn autonomously, and that the performance of the model is relatively fixed and cannot be updated online after it is deployed to the edge, this paper proposes a cloud-edge-end collaborative fault diagnosis method based on a deep residual shrinkage adaptive network.
First, the historical data of known operating conditions is collected and a deep residual shrinkage adaptive network model is built in the cloud through which reinforcement learning algorithms are introduced. These give the model the ability to update adaptively and learn data online under changing working conditions, thereby realizing online updating and adaptive performance enhancement. Model deployment and updating at the edge end are then realized by model slice distribution and edge slice aggregation, and real-time fault diagnosis is performed at the edge. Finally, the effectiveness of the proposed method is verified using a ship propulsion shaft system experimental bench.
The results show that the proposed method is able to realize the online updating of the model under variable operating conditions, and the updated model has higher fault diagnosis accuracy compared with a non-updated model.
The results of this study can provide useful references for the fault diagnosis of propulsion shaft systems under variable operating conditions.
| [1] |
ZHOU J, YANG Y, DING S X, et al. A fault detection and health monitoring scheme for ship propulsion systems using SVM technique[J]. IEEE Access, 2018, 6: 16207–16215. doi: 10.1109/ACCESS.2018.2812207
|
| [2] |
王瑞涵, 陈辉, 管聪, 等. 基于图卷积网络的非均衡数据船舶柴油机故障诊断[J]. 中国舰船研究, 2022, 17(5): 289–300. doi: 10.19693/j.issn.1673-3185.02859
WANG R H, CHEN H, GUAN C, et al. Fault diagnosis of marine diesel engines based on graph convolutional network under unbalanced datasets[J]. Chinese Journal of Ship Research, 2022, 17(5): 289–300 (in Chinese). doi: 10.19693/j.issn.1673-3185.02859
|
| [3] |
LEI Y G, YANG B, JIANG X W, et al. Applications of machine learning to machine fault diagnosis: a review and roadmap[J]. Mechanical Systems and Signal Processing, 2020, 138: 106587. doi: 10.1016/j.ymssp.2019.106587
|
| [4] |
JIANG W X, WU J, ZHU H P, et al. Paired ensemble and group knowledge measurement for health evaluation of wind turbine gearbox under compound fault scenarios[J]. Journal of Manufacturing Systems, 2023, 70: 382–394. doi: 10.1016/j.jmsy.2023.08.004
|
| [5] |
徐鹏, 杨海燕, 程宁, 等. 基于优化BP神经网络的船舶动力系统故障诊断[J]. 中国舰船研究, 2021, 16(增刊1): 106–113.
XU P, YANG H Y, CHENG N, et al. Fault diagnosis of ship power system based on optimized BP neural network[J]. Chinese Journal of Ship Research, 2021, 16(Supp 1): 106–113 (in Chinese).
|
| [6] |
宫文峰, 陈辉, WANG D W. 基于改进LSTM-SVM的多传感器船舶旋转机械快速故障诊断方法[J]. 船舶力学, 2021, 25(9): 1239–1250.
GONG W F, CHEN H, WANG D W. Fast fault diagnosis method of marine rotating machinery with multi-sensor monitoring based on improved LSTM-SVM[J]. Journal of Ship Mechanics, 2021, 25(9): 1239–1250 (in Chinese).
|
| [7] |
LI G Q, WU J, DENG C, et al. Convolutional neural network-based Bayesian Gaussian mixture for intelligent fault diagnosis of rotating machinery[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 3517410.
|
| [8] |
SHAO H D, XIA M, WAN J F, et al. Modified stacked autoencoder using adaptive morlet wavelet for intelligent fault diagnosis of rotating machinery[J]. IEEE/ASME Transactions on Mechatronics, 2022, 27(1): 24–33. doi: 10.1109/TMECH.2021.3058061
|
| [9] |
王辉, 徐佳文, 严如强. 基于多尺度注意力深度强化学习网络的行星齿轮箱智能诊断方法[J]. 机械工程学报, 2022, 58(11): 133–142. doi: 10.3901/JME.2022.11.133
WANG H, XU J W, YAN R Q. Multi-scale attention based deep reinforcement learning for intelligent fault diagnosis of planetary gearbox[J]. Journal of Mechanical Engineering, 2022, 58(11): 133–142 (in Chinese). doi: 10.3901/JME.2022.11.133
|
| [10] |
王超, 田波, 李子睿, 等. 基于自注意力子域自适应对抗网络的轴承变工况故障诊断方法[J]. 中国舰船研究, 2023, 18(5): 260–268. doi: 10.19693/j.issn.1673-3185.03092
WANG C, TIAN B, LI Z R, et al. Self-attention and subdomain adaptive adversarial network for bearing fault diagnosis under varying operation conditions[J]. Chinese Journal of Ship Research, 2023, 18(5): 260–268 (in Chinese). doi: 10.19693/j.issn.1673-3185.03092
|
| [11] |
LAI Y H, WU T C, LAI C F, et al. Cognitive optimal-setting control of AIoT industrial applications with deep reinforcement learning[J]. IEEE Transactions on Industrial Informatics, 2021, 17(3): 2116–2123. doi: 10.1109/TII.2020.2986501
|
| [12] |
WANG H, XU J W, SUN C, et al. Intelligent fault diagnosis for planetary gearbox using time-frequency representation and deep reinforcement learning[J]. IEEE/ASME Transactions on Mechatronics, 2022, 27(2): 985–998. doi: 10.1109/TMECH.2021.3076775
|
| [13] |
IBRAHIM A M, YAU K L A, CHONG Y W, et al. Applications of multi-agent deep reinforcement learning: models and algorithms[J]. Applied Sciences, 2021, 11(22): 10870. doi: 10.3390/app112210870
|
| [14] |
WEN L, LI X Y, GAO L. A new reinforcement learning based learning rate scheduler for convolutional neural network in fault classification[J]. IEEE Transactions on Industrial Electronics, 2021, 68(12): 12890–12900. doi: 10.1109/TIE.2020.3044808
|
| [15] |
辛阔, 王建国, 张文兴. 基于深度Q学习策略的旋转机械故障诊断方法研究[J]. 机电工程, 2021, 38(10): 1261–1268.
XIN K, WANG J G, ZHANG W X. Rotating machinery fault diagnosis method based on depth Q learning strategy[J]. Journal of Mechanical & Electrical Engineering, 2021, 38(10): 1261–1268 (in Chinese).
|
| [16] |
DING Y, MA L, MA J, et al. Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: a deep reinforcement learning approach[J]. Advanced Engineering Informatics, 2019, 42: 100977. doi: 10.1016/j.aei.2019.100977
|
| [17] |
许萌萌, 张成伟, 梅顺峰, 等. 基于数据大脑的船岸一体机舱智能运维系统研究设计[J]. 中国舰船研究, 2022, 17(6): 79–87. doi: 10.19693/j.issn.1673-3185.02642
XU M M, ZHANG C W, MEI S F, et al. Research and design of ship-shore integrated engine room intelligent operation and maintenance system based on data brain[J]. Chinese Journal of Ship Research, 2022, 17(6): 79–87 (in Chinese). doi: 10.19693/j.issn.1673-3185.02642
|
| [18] |
LU S L, LU J F, AN K, et al. Edge computing on IoT for machine signal processing and fault diagnosis: a review[J]. IEEE Internet of Things Journal, 2023, 10(13): 11093–11116. doi: 10.1109/JIOT.2023.3239944
|
| [19] |
叶宗真, 余凯伟, 张佳卿, 等. 基于TDengine的机械设备边云协同健康监测系统设计[J]. 兵器装备工程学报, 2023, 44(8): 249–256.
YE Z Z, YU K W, ZHANG J Q, et al. Design of TDengine-based edge-cloud collaborative health monitoring system for mechanical equipment[J]. Journal of Ordnance Equipment Engineering, 2023, 44(8): 249–256 (in Chinese).
|
| [20] |
REN L, JIA Z D, WANG T, et al. LM-CNN: a cloud-edge collaborative method for adaptive fault diagnosis with label sampling space enlarging[J]. IEEE Transactions on Industrial Informatics, 2022, 18(12): 9057–9067. doi: 10.1109/TII.2022.3180389
|
| [21] |
HUANG K K, TAO Z, WANG C, et al. Cloud-edge collaborative method for industrial process monitoring based on error-triggered dictionary learning[J]. IEEE Transactions on Industrial Informatics, 2022, 18(12): 8957–8966. doi: 10.1109/TII.2022.3161640
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
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