Remaining Useful Life Prediction Using Constraint Guided Learning with Limited Physical Knowledge

Abstract

Unexpected failures in industrial assets can cause significant downtime and costs. Anticipating the Remaining Useful Life (RUL) enables proactive maintenance strategies that mitigate such risks. While deep learning models have proven themselves to be adept at RUL prediction, they do not inherently guarantee physically consistent predictions. This paper explores two alternative approaches for incorporating physical constraints into data-driven RUL prediction models. The first approach extends prior work on physics-guided loss functions by integrating domain knowledge directly into the training objective. Physical assumptions are encoded as additional penalty terms that act as regularizers, discouraging physically implausible behavior while allowing trade-offs with predictive accuracy. The second approach leverages Constraint-Guided Gradient Descent (CGGD), a recent optimization framework which enforces constraints at the optimization level rather than through the loss function. CGGD monitors constraint satisfaction during training and dynamically modifies gradient updates only when violations occur, steering the solution back into the feasible region. Both methods aim to improve model interpretability and robustness without requiring detailed or fully specified physical knowledge, making them applicable to a wide range of industrial settings. We evaluate
these strategies across multiple experimental setups, comparing standard predictive accuracy with additional physics-based evaluation metrics that assess adherence to physical assumptions. The findings provide useful insights into the advantages and limitations of constraint enforcement techniques and their
overall impact on predictions, contributing to the development of trustworthy Prognostics and Health Management (PHM) models.