EverAdapt: Continuous Adaptation for Dynamic Machine Fault Diagnosis Environments
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913443906322432 |
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| author | Edward Ragab, Mohamed Xu, Yuecong Wu, Min Xu, Yuecong Chen, Zhenghua Alseiari, Abdulla Li, Xiaoli |
| author_facet | Edward Ragab, Mohamed Xu, Yuecong Wu, Min Xu, Yuecong Chen, Zhenghua Alseiari, Abdulla Li, Xiaoli |
| contents | Unsupervised Domain Adaptation (UDA) has emerged as a key solution in data-driven fault diagnosis, addressing domain shift where models underperform in changing environments. However, under the realm of continually changing environments, UDA tends to underperform on previously seen domains when adapting to new ones - a problem known as catastrophic forgetting. To address this limitation, we introduce the EverAdapt framework, specifically designed for continuous model adaptation in dynamic environments. Central to EverAdapt is a novel Continual Batch Normalization (CBN), which leverages source domain statistics as a reference point to standardize feature representations across domains. EverAdapt not only retains statistical information from previous domains but also adapts effectively to new scenarios. Complementing CBN, we design a class-conditional domain alignment module for effective integration of target domains, and a Sample-efficient Replay strategy to reinforce memory retention. Experiments on real-world datasets demonstrate EverAdapt superiority in maintaining robust fault diagnosis in dynamic environments. Our code is available: https://github.com/mohamedr002/EverAdapt |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17117 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EverAdapt: Continuous Adaptation for Dynamic Machine Fault Diagnosis Environments Edward Ragab, Mohamed Xu, Yuecong Wu, Min Xu, Yuecong Chen, Zhenghua Alseiari, Abdulla Li, Xiaoli Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Unsupervised Domain Adaptation (UDA) has emerged as a key solution in data-driven fault diagnosis, addressing domain shift where models underperform in changing environments. However, under the realm of continually changing environments, UDA tends to underperform on previously seen domains when adapting to new ones - a problem known as catastrophic forgetting. To address this limitation, we introduce the EverAdapt framework, specifically designed for continuous model adaptation in dynamic environments. Central to EverAdapt is a novel Continual Batch Normalization (CBN), which leverages source domain statistics as a reference point to standardize feature representations across domains. EverAdapt not only retains statistical information from previous domains but also adapts effectively to new scenarios. Complementing CBN, we design a class-conditional domain alignment module for effective integration of target domains, and a Sample-efficient Replay strategy to reinforce memory retention. Experiments on real-world datasets demonstrate EverAdapt superiority in maintaining robust fault diagnosis in dynamic environments. Our code is available: https://github.com/mohamedr002/EverAdapt |
| title | EverAdapt: Continuous Adaptation for Dynamic Machine Fault Diagnosis Environments |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2407.17117 |