Physics-informed Domain Adaptation for Health State Assessment of Industrial Components

The assessment of equipment health during field operation is at the basis of condition-based and predictive maintenance. However, in many cases, Health Indicators (HIs) can only be measured in controlled environments, such as laboratories. The difference in the conditions of field operation and the controlled environment creates a domain shift that complicates health state estimation. To address this, we propose a novel Physics-informed Domain Adaptation (PIDA) method that uses data collected in controlled environment (source domain) to estimate the equipment HI in the field (target domain). The method assumes that shifts in operating conditions induce localized perturbations in the degradation dynamics, while the physical laws governing the degradation process remain invariant. PIDA first utilizes Sparse Identification of Nonlinear Dynamics with control (SINDYc) to discover the degradation laws governing the source domain data. Subsequently, a model is trained using a composite loss function that combines a Maximum Mean Discrepancy (MMD) term for domain alignment with a physics-informed term that constrains the target domain HI estimates to conform to the discovered law. We validate PIDA on the Aramis Data Challenge and a cross-domain case study involving lithium-ion batteries. PIDA is shown to achieve an average Root Mean Squared Error reduction of 16.95% in HI estimation compared to the best-performing among the state-of-the-art methods considered.