Title :
ARL criterion in Bayesian process control using hidden Markov model
Author :
Jiang, Rui ; Makis, Viliam
Author_Institution :
Dept. of Mech. & Ind. Eng., Univ. of Toronto, Toronto, ON, Canada
Abstract :
In this paper, a multivariate Bayesian control chart is designed for a condition-based maintenance application. The system deterioration process is modeled as a 3-state hidden Markov process, with good, warning and failure states. Bayesian control chart is then applied to monitor the process by plotting the posterior probability that the system is in the warning state. It has been shown in the literature that Bayesian control chart is an optimal tool for statistical process control unlike traditional control charts. In this paper, a new fault detection scheme is developed based on the average run length criterion. Comparison results with former control chart methods are provided to illustrate the effectiveness of this approach.
Keywords :
Bayes methods; control charts; hidden Markov models; process monitoring; statistical process control; Bayesian process control; average run length criterion; condition-based maintenance application; fault detection scheme; hidden Markov model; multivariate Bayesian control chart; posterior probability; statistical process control; system deterioration process; Bayesian methods; Computerized monitoring; Condition monitoring; Control charts; Fault detection; Hidden Markov models; Industrial engineering; Optimal control; Probability; Process control; Average run length; Bayesian control chart; condition-based maintenance; hidden Markov model;
Conference_Titel :
Industrial Engineering and Engineering Management, 2009. IEEM 2009. IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-4869-2
Electronic_ISBN :
978-1-4244-4870-8
DOI :
10.1109/IEEM.2009.5373379
