Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure

Kim, Michael Jong; Jiang, Rui; Makiš, Viliam; Lee, Chi-Guhn

doi:10.1016/j.ejor.2011.04.023

Cited by 77 publications

(51 citation statements)

References 15 publications

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“…Note that using only two operational states is sufficient in most practical applications (see, e.g., [16]). In many cases, the system deterioration is gradual, but to detect a severe system condition, it is important to define only two distinct phases.…”

Section: Model Formulationmentioning

confidence: 99%

Optimal condition-based maintenance policy for a partially observable system with two sampling intervals

Makiš

2014

Int J Adv Manuf Technol

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In this paper, we propose an optimal Bayesian control policy with two sampling intervals minimizing the long-run expected average maintenance cost per unit time for a partially observable deteriorating system. Unlike the previous optimal Bayesian approaches which used periodic sampling models with equidistant intervals, a novel sampling methodology is proposed which is characterized by two sampling intervals and two control thresholds. The deterioration process is modeled as a 3-state continuous time hidden-Markov process with two unobservable operatingstates and an observable failure state. At each sampling epoch, the multivariate observation data provides only partial information about the actual state of the system. We start observing the system with a longer sampling interval. If the posterior probability that the system is in the warning state exceeds a warning limit, observations are taken more frequently, i.e., the sampling interval changes to a shorter one, and if the posterior probability exceeds a maintenance limit, the full inspection is performed, followed possibly by preventive maintenance. We formulate the maintenance control problem in a partially observable Markov decision process (POMDP) framework to find the two optimal control limits and two sampling intervals. Also, the mean residual life (MRL) of the system is calculated as a function of the posterior probability. A numerical example is provided and comparison of the proposed scheme with several alternative sampling and maintenance control strategies is carried out.F. Naderkhani ZG · V. Makis ( ) Mechanical and Industrial Engineering

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Section: Model Formulationmentioning

confidence: 99%

Optimal condition-based maintenance policy for a partially observable system with two sampling intervals

Makiš

2014

Int J Adv Manuf Technol

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“…We assume two operational states and one absorbing state for the covariate process. In most practical applications (see, for example, Kim et al, 2011), considering only two operational states is sufficient for fault detection and CBM. The first phase is the normal or healthy phase where the measurements of the covariate process obtained from CM behave approximately as a stationary process.…”

Section: Model Formulationmentioning

confidence: 99%

“…Among different approaches, CBM has been applied widely in various industries depending on the collected CM data, including oil data in Wang and Hussin (2009) and Kim et al (2011) or vibration data in Yam et al (2001) and Tian et al (2014). In a CBM program, maintenance action is chosen based on the information collected through CM .…”

Section: Introductionmentioning

confidence: 99%

Joint optimization of maintenance policy and inspection interval for a multi-unit series system using proportional hazards model

Jafari

Naderkhani

Makiš

2017

Journal of the Operational Research Society

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Unlike the previous maintenance models of multi-unit systems which considered condition-based maintenance (CBM) or age information separately, we propose a novel optimization model which is characterized by a combination of CBM and age information using proportional hazards model. The preventive maintenance is applied for the main two units, where one unit is the core part of the system and subject to CM, and only the age information for the second main unit is available. Also, the other units are adjusted or replaced each time when the system is maintained. The objective is to find an optimal opportunistic maintenance policy minimizing the long-run expected average cost per unit time. The problem is formulated and solved in the semi-Markov decision process framework. The formula for the mean residual life of the system is derived, which is an important statistic in practical applications. A practical example of a multi-unit system from a mining company is provided, and a comparison with other policies shows an outstanding performance of the new model and the control policy proposed in this paper.

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“…The described technique will be termed probabilistic forecasting (PF) [9][10][11][12][13][14]. We emphasize that the above hypotheses regarding the stochastic properties of real datasets are almost impossible to verify, especially under small data arrays.…”

Section: Introductionmentioning

confidence: 99%

New Method of Randomized Forecasting Using Entropy-Robust Estimation: Application to the World Population Prediction

2016

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Abstract:We propose a new method of randomized forecasting (RF-method), which operates with models described by systems of linear ordinary differential equations with random parameters. The RF-method is based on entropy-robust estimation of the probability density functions (PDFs) of model parameters and measurement noises. The entropy-optimal estimator uses a limited amount of data. The method of randomized forecasting is applied to World population prediction. Ensembles of entropy-optimal prognostic trajectories of World population and their probability characteristics are generated. We show potential preferences of the proposed method in comparison with existing methods.

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Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure

Cited by 77 publications

References 15 publications

Optimal condition-based maintenance policy for a partially observable system with two sampling intervals

Optimal condition-based maintenance policy for a partially observable system with two sampling intervals

Joint optimization of maintenance policy and inspection interval for a multi-unit series system using proportional hazards model

New Method of Randomized Forecasting Using Entropy-Robust Estimation: Application to the World Population Prediction

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