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

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  • Ning Wang
  • Shu-dong Sun
  • Zhi-qiang Cai
  • Shuai Zhang
  • Can Saygin

Abstract

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.

Suggested Citation

  • Ning Wang & Shu-dong Sun & Zhi-qiang Cai & Shuai Zhang & Can Saygin, 2014. "A Hidden Semi-Markov Model with Duration-Dependent State Transition Probabilities for Prognostics," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, April.
    • Handle: RePEc:hin:jnlmpe:632702
    DOI: 10.1155/2014/632702
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