Residual-life distributions from component degradation signals: A Bayesian approach

Nagi Z. Gebraeel, Mark A. Lawley, Rong Li, Jennifer K. Ryan

Research output: Contribution to journalArticlepeer-review

533 Scopus citations

Abstract

Real-time condition monitoring is becoming an important tool in maintenance decision-making. Condition monitoring is the process of collecting real-time sensor information from a functioning device in order to reason about the health of the device. To make effective use of condition information, it is useful to characterize a device degradation signal, a quantity computed from condition information that captures the current state of the device and provides information on how that condition is likely to evolve in the future. If properly modeled, the degradation signal can be used to compute a residual-life distribution for the device being monitored, which can then be used in decision models. In this work, we develop Bayesian updating methods that use real-time condition monitoring information to update the stochastic parameters of exponential degradation models. We use these degradation models to develop a closed-form residual-life distribution for the monitored device. Finally, we apply these degradation and residual-life models to degradation signals obtained through the accelerated testing of bearings.

Original languageEnglish (US)
Pages (from-to)543-557
Number of pages15
JournalIIE Transactions (Institute of Industrial Engineers)
Volume37
Issue number6
DOIs
StatePublished - Jun 2005

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Fingerprint Dive into the research topics of 'Residual-life distributions from component degradation signals: A Bayesian approach'. Together they form a unique fingerprint.

Cite this