Predictive Maintenance by Risk Sensitive Particle Filtering

Predictive Maintenance (PrM) exploits the estimation of the equipment Residual Useful Life (RUL) to identify the optimal time for carrying out the next maintenance action. Particle Filtering (PF) is widely used as a prognostic tool in support of PrM, by reason of its capability of robustly estimating the equipment RUL without requiring strict modeling hypotheses. However, a precise PF estimate of the RUL requires tracing a large number of particles, and thus large computational times, often incompatible with the need of rapidly processing information for making maintenance decisions in due time. This work considers two different Risk Sensitive Particle Filtering (RSPF) schemes proposed in the literature, and investigates their potential for PrM. The computational burden problem of PF is addressed. The effectiveness of the two algorithms is analyzed on a case study concerning a mechanical component affected by fatigue degradation.