An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process

Ramin Moghaddass, Ming J. Zuo

Research output: Contribution to journalArticle

75 Scopus citations

Abstract

Efficient asset management is of paramount importance, particularly for systems with costly downtime and failure. As in energy and capital-intensive industries, the economic loss of downtime and failure is huge, the need for a low-cost and integrated health monitoring system has increased significantly over the years. Timely detection of faults and failures through an efficient prognostics and health management (PHM) framework can lead to appropriate maintenance actions to be scheduled proactively to avoid catastrophic failures and minimize the overall maintenance cost of the systems. This paper aims at practical challenges of online diagnostics and prognostics of mechanical systems under unobservable degradation. First, the elements of a multistate degradation structure are reviewed and then a model selection framework is introduced. Important dynamic performance measures are introduced, which can be used for online diagnostics and prognostics. The effectiveness of the result of this paper is demonstrated with a case study on the health monitoring of turbofan engines.

Original languageEnglish (US)
Pages (from-to)92-104
Number of pages13
JournalReliability Engineering and System Safety
Volume124
DOIs
StatePublished - Apr 2014

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Keywords

  • Condition monitoring
  • Model selection
  • Multistate degradation process
  • Online diagnostics and prognostics
  • Reliability analysis

ASJC Scopus subject areas

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

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