Deep learning (DL) models have gained significant popularity for the prognostics of systems experiencing degradation. However, there are two major concerns with such models. Firstly, they require a substantial amount of training data due to their large number of parameters. Secondly, they disregard the underlying physics and solely fit the available data, leading to potentially weak generalization capabilities when faced with unseen out-of-distribution data in the field. This study aims to tackle these challenges by incorporating the underlying physics of degradation into DL models. The objective is to develop a novel DL-based approach in conjunction with Bayesian filtering, enabling physics-informed probabilistic life prediction for systems subject to environmentally induced degradation. The proposed framework consists of two main components: physics discovery and degradation prediction. The former involves identifying the dominant stress agents and formulating the underlying physics of degradation. The latter predicts the degradation of the system by incorporating the discovered physics into a DL model. It is expected the results indicate that by combining data-driven DL with physics-based insights, more robust and reliable life predictions can be achieved, addressing the limitations of DL approaches. This framework holds promise for enhancing decision-making processes related to maintenance strategies in various industries.
How to Cite
deep learning, physics of failure, physics-informed model
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