Remaining Useful Life Prediction via Computation of Physical and Material Properties

Remaining useful life (RUL) prediction is a crucial aspect of predictive maintenance, enabling industries to optimize asset longevity and minimize unexpected failures. RUL becomes increasingly difficult with system complexity. Such systems are built up of components exhibiting known material properties. When degradation occurs, these materials are predictably affected and can indicate the remaining life of the system, both directly and indirectly. Changes reflected from damage, ageing and wear are particularly detectable in stiffness of materials for example. This paper aims to utilize the physics and domain knowledge of systems along with material degradation indicators such as Young’s Modulus and the interdependencies between such metrics, to accomplish RUL with a deeper understanding of wear and fatigue as compared to purely data-driven methods. Experimental studies and computational simulations demonstrate the effectiveness of this approach, offering a novel perspective on predictive maintenance strategies.