Health Assessment and Prognostics Based on Higher Order Hidden Semi-Markov Models

TL;DR

This paper introduces a higher order hidden semi-Markov model (HOHSMM) for health assessment and prognostics, capable of modeling complex transition dynamics and unobservable states, with demonstrated effectiveness on NASA turbofan engine data.

Contribution

The paper develops a novel HOHSMM framework with a Gibbs sampling inference algorithm for improved health state estimation and RUL prediction in complex systems.

Findings

HOHSMM accurately estimates hidden health states.

The framework effectively predicts remaining useful life.

Demonstrated success on NASA turbofan engine data.

Abstract

This paper presents a new and flexible prognostics framework based on a higher order hidden semi-Markov model (HOHSMM) for systems or components with unobservable health states and complex transition dynamics. The HOHSMM extends the basic hidden Markov model (HMM) by allowing the hidden state to depend on its more distant history and assuming generally distributed state duration. An effective Gibbs sampling algorithm is designed for statistical inference of an HOHSMM. The performance of the proposed HOHSMM sampler is evaluated by conducting a simulation experiment. We further design a decoding algorithm to estimate the hidden health states using the learned model. Remaining useful life (RUL) is predicted using a simulation approach given the decoded hidden states. The practical utility of the proposed prognostics framework is demonstrated by a case study on NASA turbofan engines. The results show that the HOHSMM-based prognostics framework provides good hidden health state assessment and RUL estimation for complex systems.