A ResNet-based deep reinforcement learning framework using soft actor-critic for remaining useful life prediction of rolling bearings
Accurately predicting the Remaining Useful Life (RUL) of machinery plays important role for implementing effective predictive maintenance strategies and reducing downtime. However, many existing data-driven approaches rely heavily on supervised learning and treat RUL estimation as a direct regressio...
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| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025028063 |
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| author | Thanh Tung Luu Duy An Huynh |
| author_facet | Thanh Tung Luu Duy An Huynh |
| author_sort | Thanh Tung Luu |
| collection | DOAJ |
| description | Accurately predicting the Remaining Useful Life (RUL) of machinery plays important role for implementing effective predictive maintenance strategies and reducing downtime. However, many existing data-driven approaches rely heavily on supervised learning and treat RUL estimation as a direct regression task from sensor data, lacking the ability to model temporal decision-making or adapt to different domains. To address these limitations, this study proposes a deep reinforcement learning framework that integrates a ResNet-based autoencoder for latent feature extraction from raw vibration signals with the Soft Actor-Critic (SAC) algorithm for dynamic RUL prediction. Unlike conventional methods, our approach allows the model to learn RUL dynamically by interacting with the environment, rather than passively mapping inputs to targets. This interaction enables better adaptability to uncertain degradation patterns. Experimental results on the PHM 2012 dataset demonstrate that the proposed SAC-ResNet framework achieves superior accuracy and generalization performance, highlighting its potential as a promising alternative to traditional RUL estimation models. |
| format | Article |
| id | doaj-art-5f047e1b4d6242099acb77abf20bb01b |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-5f047e1b4d6242099acb77abf20bb01b2025-08-22T04:57:24ZengElsevierResults in Engineering2590-12302025-09-012710673910.1016/j.rineng.2025.106739A ResNet-based deep reinforcement learning framework using soft actor-critic for remaining useful life prediction of rolling bearingsThanh Tung Luu0Duy An Huynh1Department of Construction Machinery and Handling Equipment, Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam; Vietnam National University Ho Chi Minh City, Ho Chi Minh City, VietnamDepartment of Construction Machinery and Handling Equipment, Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam; Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam; Corresponding author at: Department of Construction Machinery and Handling Equipment, Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam.Accurately predicting the Remaining Useful Life (RUL) of machinery plays important role for implementing effective predictive maintenance strategies and reducing downtime. However, many existing data-driven approaches rely heavily on supervised learning and treat RUL estimation as a direct regression task from sensor data, lacking the ability to model temporal decision-making or adapt to different domains. To address these limitations, this study proposes a deep reinforcement learning framework that integrates a ResNet-based autoencoder for latent feature extraction from raw vibration signals with the Soft Actor-Critic (SAC) algorithm for dynamic RUL prediction. Unlike conventional methods, our approach allows the model to learn RUL dynamically by interacting with the environment, rather than passively mapping inputs to targets. This interaction enables better adaptability to uncertain degradation patterns. Experimental results on the PHM 2012 dataset demonstrate that the proposed SAC-ResNet framework achieves superior accuracy and generalization performance, highlighting its potential as a promising alternative to traditional RUL estimation models.http://www.sciencedirect.com/science/article/pii/S2590123025028063AutoencoderResidual networksReinforcement learningSoft actor-criticRemaining useful life (RUL) |
| spellingShingle | Thanh Tung Luu Duy An Huynh A ResNet-based deep reinforcement learning framework using soft actor-critic for remaining useful life prediction of rolling bearings Results in Engineering Autoencoder Residual networks Reinforcement learning Soft actor-critic Remaining useful life (RUL) |
| title | A ResNet-based deep reinforcement learning framework using soft actor-critic for remaining useful life prediction of rolling bearings |
| title_full | A ResNet-based deep reinforcement learning framework using soft actor-critic for remaining useful life prediction of rolling bearings |
| title_fullStr | A ResNet-based deep reinforcement learning framework using soft actor-critic for remaining useful life prediction of rolling bearings |
| title_full_unstemmed | A ResNet-based deep reinforcement learning framework using soft actor-critic for remaining useful life prediction of rolling bearings |
| title_short | A ResNet-based deep reinforcement learning framework using soft actor-critic for remaining useful life prediction of rolling bearings |
| title_sort | resnet based deep reinforcement learning framework using soft actor critic for remaining useful life prediction of rolling bearings |
| topic | Autoencoder Residual networks Reinforcement learning Soft actor-critic Remaining useful life (RUL) |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025028063 |
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