FRAM-based health monitoring for high reliability equipment considering function–performance coupling effects

Accurate health-state estimation is critical for the reliable operation and maintenance of high-reliability equipment. However, intrinsic function–performance coupling can distort state evolution and reduce monitoring credibility. We introduce a coupling-aware framework that (i) detects and quantifies latent couplings with the Function Resonance Analysis Method (FRAM); (ii) embeds these effects as hidden variables in an augmented discrete state-space model; and (iii) performs Bayesian, real-time inference via a particle filter that remains stable under nonlinear, time-varying conditions. Applied to steam-turbine rotor monitoring, the method consistently tracks crack growth, lowers remaining-useful-life prediction error, and preserves estimation robustness when task loads or functional configurations shift. The results demonstrate its practicality for complex, strongly coupled systems and its potential for broader prognostics and health management applications.