Degradation modeling for real-time estimation of residual lifetimes in dynamic environments

This article presents a methodology for modeling degradation signals from components functioning under dynamically evolving environment conditions. In situ sensor signals related to the degradation process are utilized as well as the environment conditions, to predict and update, in real-time, the d...

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Bibliographic Details
Published inIIE transactions Vol. 47; no. 5; pp. 471 - 486
Main Authors Bian, Linkan, Gebraeel, Nagi, Kharoufeh, Jeffrey P.
Format Journal Article
LanguageEnglish
Published Norcross Taylor & Francis 04.05.2015
Taylor & Francis Ltd
Subjects
Bayesian analysis
Bayesian estimation
Degradation
Degradation model
Dynamics
Markov analysis
Mathematical models
Real time
reliability
residual lifetime distribution
Sensors
Signal processing
Studies
Unions
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Summary:This article presents a methodology for modeling degradation signals from components functioning under dynamically evolving environment conditions. In situ sensor signals related to the degradation process are utilized as well as the environment conditions, to predict and update, in real-time, the distribution of a component's residual lifetime. The model assumes that the time-dependent rate at which a component's degradation signal increases (or decreases) is affected by the severity of the current environmental or operational conditions. These conditions are assumed to evolve as a continuous-time Markov chain. Unique to the proposed model is the union of historical data with real-time, sensor-based data to update the signal parameters, environment parameters, and the residual lifetime distribution of the component within a Bayesian framework.
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ISSN:0740-817X
2472-5854
1545-8830
2472-5862
DOI:10.1080/0740817X.2014.955153