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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.21207 |
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| _version_ | 1866912604988899328 |
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| author | Fink, Olga Nejjar, Ismail Sharma, Vinay Niresi, Keivan Faghih Sun, Han Dong, Hao Xu, Chenghao Wei, Amaury Bizzi, Arthur Theiler, Raffael Tian, Yuan Von Krannichfeldt, Leandro Ma, Zhan Garmaev, Sergei Zhang, Zepeng Zhao, Mengjie |
| author_facet | Fink, Olga Nejjar, Ismail Sharma, Vinay Niresi, Keivan Faghih Sun, Han Dong, Hao Xu, Chenghao Wei, Amaury Bizzi, Arthur Theiler, Raffael Tian, Yuan Von Krannichfeldt, Leandro Ma, Zhan Garmaev, Sergei Zhang, Zepeng Zhao, Mengjie |
| contents | Prognostics and Health Management ensures the reliability, safety, and efficiency of complex engineered systems by enabling fault detection, anticipating equipment failures, and optimizing maintenance activities throughout an asset lifecycle. However, real-world PHM presents persistent challenges: sensor data is often noisy or incomplete, available labels are limited, and degradation behaviors and system interdependencies can be highly complex and nonlinear. Physics-informed machine learning has emerged as a promising approach to address these limitations by embedding physical knowledge into data-driven models. This review examines how incorporating learning and observational biases through physics-informed modeling and data strategies can guide models toward physically consistent and reliable predictions. Learning biases embed physical constraints into model training through physics-informed loss functions and governing equations, or by incorporating properties like monotonicity. Observational biases influence data selection and synthesis to ensure models capture realistic system behavior through virtual sensing for estimating unmeasured states, physics-based simulation for data augmentation, and multi-sensor fusion strategies. The review then examines how these approaches enable the transition from passive prediction to active decision-making through reinforcement learning, which allows agents to learn maintenance policies that respect physical constraints while optimizing operational objectives. This closes the loop between model-based predictions, simulation, and actual system operation, empowering adaptive decision-making. Finally, the review addresses the critical challenge of scaling PHM solutions from individual assets to fleet-wide deployment. Fast adaptation methods including meta-learning and few-shot learning are reviewed alongside domain generalization techniques ... |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21207 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM Fink, Olga Nejjar, Ismail Sharma, Vinay Niresi, Keivan Faghih Sun, Han Dong, Hao Xu, Chenghao Wei, Amaury Bizzi, Arthur Theiler, Raffael Tian, Yuan Von Krannichfeldt, Leandro Ma, Zhan Garmaev, Sergei Zhang, Zepeng Zhao, Mengjie Machine Learning Prognostics and Health Management ensures the reliability, safety, and efficiency of complex engineered systems by enabling fault detection, anticipating equipment failures, and optimizing maintenance activities throughout an asset lifecycle. However, real-world PHM presents persistent challenges: sensor data is often noisy or incomplete, available labels are limited, and degradation behaviors and system interdependencies can be highly complex and nonlinear. Physics-informed machine learning has emerged as a promising approach to address these limitations by embedding physical knowledge into data-driven models. This review examines how incorporating learning and observational biases through physics-informed modeling and data strategies can guide models toward physically consistent and reliable predictions. Learning biases embed physical constraints into model training through physics-informed loss functions and governing equations, or by incorporating properties like monotonicity. Observational biases influence data selection and synthesis to ensure models capture realistic system behavior through virtual sensing for estimating unmeasured states, physics-based simulation for data augmentation, and multi-sensor fusion strategies. The review then examines how these approaches enable the transition from passive prediction to active decision-making through reinforcement learning, which allows agents to learn maintenance policies that respect physical constraints while optimizing operational objectives. This closes the loop between model-based predictions, simulation, and actual system operation, empowering adaptive decision-making. Finally, the review addresses the critical challenge of scaling PHM solutions from individual assets to fleet-wide deployment. Fast adaptation methods including meta-learning and few-shot learning are reviewed alongside domain generalization techniques ... |
| title | From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.21207 |