PHYSICS-INFORMED MACHINE LEARNING FOR RELIABILITY AND REMAINING USEFUL LIFE PREDICTION OF AEROSPACE SYSTEMS

Abstract

Aerospace systems demand exceptional reliability standards due to safety-critical operationsand high consequence of failure. Traditional reliability assessment and remaining useful life(RUL) prediction methods rely heavily on either physics-based models requiring extensivecomputational resources or purely data-driven approaches that lack physical interpretability and struggle with limited failure data