Stochastic models for degradation-based reliability

By Jeffrey P. Kharoufeh, Steven M. Cox. 2005

179 citations recorded by the source corpus.

Subfields: Mechanics of Materials, Safety, Risk, Reliability and Quality, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/935a3a265bcf1311b67b9f1f418ed20d820d13bd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mechanics of Materials

In Safety, Risk, Reliability and Quality

In Statistics and Probability

Citations in the corpus

Semi-Markov models for degradation-based reliability (2010, methodology): “[ 24 ] that such a non-linear degradation path can be approximated using a piece-wise linear path”
On a Markov‐modulated shock and wear process (2009, methodology): “The existence of a minimal representation of the modulating Markov chain that leads to accurate distribution estimates was posited in [14].”
Stochastic modelling and analysis of degradation for highly reliable products (2015, background): “Kharoufeh [84] and Kharoufeh and Cox [85] assumed that the degradation rate of a system is totally dependent on random ambient environment and the evolution of the random environment is characterized as a stationary continuous-time Markov Chain.”
RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model (2017, methodology): “The commonly used models include stochastic process models [16]–[19] and Markov chain models [20].”
An age- and state-dependent Markov model for degradation processes (2011, methodology): “A generalized approach to this kind of modeling is provided by the theory of Markov additive processes (see, for example, Çinlar (1977); Kharoufeh and Cox (2005); and Kharoufeh and Mixon (2009)).”
Remaining useful life estimation - A review on the statistical data driven approaches (2011, background): “Further , Kharoufeh and Cox ( 2005 ) extended such work by consid - ering two kinds of sensors data , environment observations and degradation measures in their stochastic failure models to numer - ically compute the RUL distributions and their moments .”
Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, background): “[286,287] enriched the basic theory of Markov models in the application of RUL prediction.”