A Hidden Semi-Markov Model with Duration-Dependent State Transition Probabilities for Prognostics

Abstract EN

Realistic prognostic tools are essential for effective condition-based maintenance systems.

In this paper, a Duration-Dependent Hidden Semi-Markov Model (DD-HSMM) is proposed, which overcomes the shortcomings of traditional Hidden Markov Models (HMM), including the Hidden Semi-Markov Model (HSMM): (1) it allows explicit modeling of state transition probabilities between the states; (2) it relaxes observations’ independence assumption by accommodating a connection between consecutive observations; and (3) it does not follow the unrealistic Markov chain’s memoryless assumption and therefore it provides a more powerful modeling and analysis capability for real world problems.

To facilitate the computation of the proposed DD-HSMM methodology, new forward-backward algorithm is developed.

The demonstration and evaluation of the proposed methodology is carried out through a case study.

The experimental results show that the DD-HSMM methodology is effective for equipment health monitoring and management.