Optimal maintenance-policies for deteriorating systems under various maintenance strategies

Lam, Clifford; Yeh, Ruey Huei

doi:10.1109/24.326439

Cited by 100 publications

(45 citation statements)

References 14 publications

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“…Markov and semi-Markov models have been the preferred approach in simulation and evaluation of CBM [3][4][5]. Other approaches, like Monte Carlo modelling [6,7] or an artificial intelligence approach [8], have also been proposed by several researchers.…”

Section: Literature Reviewmentioning

confidence: 99%

A neural network application for reliability modelling and condition-based predictive maintenance

Lin

Tseng

2004

AMT

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Traditionally, decisions on the use of machinery are based on previous experience, historical data and common sense. However, carrying out an effective predictive maintenance plan, information about current machine conditions must be made known to the decision-maker. In this paper, a new method of obtaining maintenance information has been proposed. By integrating traditional reliability modelling techniques with a realtime, online performance estimation model, machine reliability information such as hazard rate and mean time between failures can be calculated. Essentially, this paper presents an innovative method to synthesise low level information (such as vibration signals) with high level information (like reliability statistics) to form a rigorous theoretical base for better machine maintenance.

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Section: Literature Reviewmentioning

confidence: 99%

A neural network application for reliability modelling and condition-based predictive maintenance

Lin

Tseng

2004

AMT

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“…Extensive reviews of such researches can be found in Refs. (Sherif and Smith 1981;Valdez-Flores and Feldman 1989;Lam and Yeh 1994;Dekker 1996;Scarf 1997;Wang 2002).…”

Section: Introductionmentioning

confidence: 98%

“…Under the Markov formulation, the sojourn time in each state is restricted to be exponentially distributed (Sherif and Smith 1981;Lam and Yeh 1994;Chiang and Yuan 2001;Yeh 1997). To have a better description of the empirical evidence, semi-Markov process have been employed to model multistate deteriorating systems (Chen and Trivedi 2005;Moustafa et al 2004;Yeh 1997;Chen and Feldman 1997) by allowing the sojourn time distribution to be non-exponential.…”

Section: Introductionmentioning

confidence: 99%

A semi-Markov decision algorithm for the optimal maintenance of a multistage deteriorating two-unit standby system

Maksoud

Moustafa

2008

Oper Res Int J

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The semi-Markov decision model is a powerful tool in analyzing sequential decision processes with random decision epochs for a multi-state deteriorating system subject to aging and fatal shocks. In this paper, we propose a model for a two-unit standby system where a cold standby unit is attached to an operating (active) one. For this model, the active unit goes through a finite number of states of successive degradation preceding the failure, while the other one is in cold standby state. At each deterioration state of the active unit, two types of maintenance are considered, minimal and major, depending on the degrading level. The minimal maintenance aims to improve the degradation of the unit by recovering it to the previous degradation stage. The maximum allowable number of minimal maintenances for all states of the active unit must not exceed a certain limit. On the other hand, the major maintenance is necessary when the active unit fails. Once this maintenance is completed, the unit is restored to as good as new. To make the system operate more time without any interruption, the standby unit can be switched online until the active unit finishes its minimal or major maintenance. The switch between the two units is perfect and switchover is instantaneous. After using the standby unit, it is serviced or overhauled to maintain it in as good as new state. We use an iterative numerical approach, based on the policy iteration method, to drive the optimal state-dependent maintenance policy that minimizes the long-run expected cost rate of the system. Finally,

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“…Many engineering systems are subject to both gradual deterioration and random shocks that cause sudden failures (Lam and Yeh, 1994). In a continuous-time setting, researchers have modeled such deterioration processes as a continuous-time Markov chain in which, from any deterioration level, transitions can be made to the next-higher deterioration level or to the failed state (Ohnishi et al, 1986;Lam and Yeh, 1994;Chiang and Yuan, 2001). In discrete time, if the deterioration level is monitored at sufficiently small time intervals and gradual deterioration is a process with stationary increments, such deterioration behavior can be captured by a transition probability matrix of the form…”

Section: I) Let G and H Be Two Probability Mass Functions If G Lr Hmentioning

confidence: 99%

Maintenance optimization for a Markovian deteriorating system with population heterogeneity

2016

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We develop a partially observable Markov decision process model to incorporate population heterogeneity when scheduling replacements for a deteriorating system. The single-component system deteriorates over a finite set of condition states according to a Markov chain. The population of spare components that is available for replacements is composed of multiple component types that cannot be distinguished by their exterior appearance but deteriorate according to different transition probability matrices. This situation may arise, for example, because of variations in the production process of components. We provide a set of conditions for which we characterize the structure of the optimal policy that minimizes the total expected discounted operating and replacement cost over an infinite horizon. In a numerical experiment, we benchmark the optimal policy against a heuristic policy that neglects population heterogeneity.

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Optimal maintenance-policies for deteriorating systems under various maintenance strategies

Cited by 100 publications

References 14 publications

A neural network application for reliability modelling and condition-based predictive maintenance

A neural network application for reliability modelling and condition-based predictive maintenance

A semi-Markov decision algorithm for the optimal maintenance of a multistage deteriorating two-unit standby system

Maintenance optimization for a Markovian deteriorating system with population heterogeneity

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