A parameter estimation method for a condition-monitored device under multi-state deterioration

Ramin Moghaddass, Ming J. Zuo

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

The overall performance of a mechanical device under random shocks, fatigue, and gradual degradation may continuously deteriorate over time, leading to multi-state health conditions. This deterioration can be represented by a continuous-time degradation process - with multiple discrete states - that reflects the relative degree of deterioration. This paper focuses on a condition-monitored device with multi-state deterioration, where its degradation state is not directly observable and only incomplete information is available through condition monitoring. After modeling this multi-state device, an unsupervised parameter estimation method is developed, which employs historical condition monitoring information to estimate the unknown characteristic parameters of the degradation process and the observation process. The results are evaluated through numerical experiments.

Original languageEnglish (US)
Pages (from-to)94-103
Number of pages10
JournalReliability Engineering and System Safety
Volume106
DOIs
StatePublished - Oct 2012
Externally publishedYes

Fingerprint

Multi-state
Deterioration
Parameter estimation
Parameter Estimation
Degradation
Condition Monitoring
Condition monitoring
Incomplete Information
Fatigue
Continuous Time
Shock
Health
Numerical Experiment
Fatigue of materials
Unknown
Modeling
Estimate
Experiments

Keywords

  • Condition monitoring
  • Continuous-time degradation process
  • Multi-state deterioration
  • Unsupervised parameter estimation

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality
  • Applied Mathematics

Cite this

A parameter estimation method for a condition-monitored device under multi-state deterioration. / Moghaddass, Ramin; Zuo, Ming J.

In: Reliability Engineering and System Safety, Vol. 106, 10.2012, p. 94-103.

Research output: Contribution to journalArticle

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