Application of recurrent neural network to mechanical fault diagnosis: A review
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Development of DQN and PPO algorithms has significantly advanced reinforcement learning, improving efficacy and applicability in complex and dynamic environments.
“This training technique has undergone significant advancements, particularly with the development of Deep Q-Networks (DQN) [178], [179], [180] and Proximal Policy Optimization (PPO) algorithms [181], [182], [183].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [178]
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McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“…sequence modeling, making them particularly effective for tasks involving language and temporal data, as their architecture is specifically designed to process sequences of data, such as text, enabling them to capture the context and order of the input effectively [150], [151], [152], [153], [154].”
Background
- Authors
- Zhu, J., Jiang, Q., Shen, Y., Qian, C., Xu, F., & Zhu, Q.
- Affiliations
- Suzhou University of Science and Technology (CN)
- Nanjing University of Posts and Telecommunications (CN)
- Venue
- Journal of Mechanical Science and Technology
- Published
- 2022-02
- Volume
- 36
- Issue
- 2
- Pages
- 527-542
- ISSN
- 1738-494X (ISSN-L)
- 1976-3824
- OpenAlex
- W4210741351
- Cited by
- 200
- References
- 110
- Citations by year
- 2026: 38 · 2025: 56 · 2024: 59 · 2023: 37 · 2022: 10
- FWCI
- 16.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Mechanical Science and Technology (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- recurrent neural networks, mechanical fault diagnosis, time-series signals, deep feature learning, hybrid neural network, nonlinear system modeling, smart manufacturing
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