Sequential Bayesian Semi Markov Framework for Mission Based Prognostics

Abstract

Prognostics focuses on predicting the Remaining Useful Life (RUL) of engineering systems, which is a key enabler for informed decision-making in Predictive Maintenance (PdM) and operational planning. While substantial research has addressed maintenance strategies, the impact of operational decisions on system degradation and RUL remains relatively underexplored. In mission-based operations, systems may be subjected to different operating profiles, each influencing degradation dynamics. Understanding how operational choices affect RUL is therefore crucial for reliable mission planning. This paper proposes a sequential Bayesian semi-Markov (SBSM) framework for prognostics that explicitly accounts for operational variability. System degradation is modelled using a hidden semi-Markov model (HSMM), a data-driven and unsupervised approach. Inference is performed via a particle filter (PF), enabling online estimation of hidden degradation states and RUL prediction under varying profiles. The proposed framework is validated using simulated degradation data. During training, multiple operational profiles are available, with each run‑to‑failure trajectory conducted under a single, fixed operating profile. In contrast, the testing phase considers degradation histories in which the operating profile changes during system operation. Results demonstrate that the proposed SBSM approach can effectively track degradation and provide meaningful RUL estimates under varying operational profiles, highlighting its potential for mission‑based prognostics.