Deep residual shrinkage adaptive network-based cloud-edge-end collaborative fault diagnosis method for propulsion shafting system

Kaiwei YU; Zirui LI; Chong CHEN; Jun WU; Haiping ZHU

doi:10.19693/j.issn.1673-3185.03779

Chinese Journal of Ship Research, Volume. 20, Issue 4, 213(2025)

Deep residual shrinkage adaptive network-based cloud-edge-end collaborative fault diagnosis method for propulsion shafting system

Kaiwei YU¹, Zirui LI², Chong CHEN³, Jun WU¹, and Haiping ZHU²

Author Affiliations

¹School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

²School of Mechanical Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

³China Ship Development and Design Center, Wuhan 430064, China

show less

Abstract Get PDF(in Chinese)

References(13)

[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, 6, 16207-16215(2018).

[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, 138, 106587(2020).

[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, 70, 382-394(2023).

[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, 70, 3517410(2021).

[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, 27, 24-33(2022).

[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, 17, 2116-2123(2021).

[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, 27, 985-998(2022).

[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, 11, 10870(2021).

[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, 68, 12890-12900(2021).

[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, 42, 100977(2019).

[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, 10, 11093-11116(2023).

[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, 18, 9057-9067(2022).

[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, 18, 8957-8966(2022).

Tools

Get Citation

Copy Citation Text

Kaiwei YU, Zirui LI, Chong CHEN, Jun WU, Haiping ZHU. 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(4): 213

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Marine Machinery, Electrical Equipment and Automation

Received: Feb. 4, 2024

Accepted: --

Published Online: Sep. 11, 2025

The Author Email:

DOI:10.19693/j.issn.1673-3185.03779

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology