Preventive Maintenance Using Continuous-Time Semi-Markov Processes

Tomasevicz, Curtis L.; Asgarpoor, Sohrab

doi:10.1109/naps.2006.360125

Cited by 19 publications

(16 citation statements)

References 6 publications

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“…In addition to a minimum volume ellipsoid , define a family of ellipsoids , where each is tight to along a direction specified by a unitary vector . 5 The shape matrix of can be obtained through the solution of the following optimization program (21) where is the set of vertices that enclose , and is the maximum length of the semi-axis in the direction [19]. The objective function is the projection of the ellipsoid onto the direction.…”

Section: Capturing Uncertainty With a Family Of Tight Upper-boundimentioning

confidence: 99%

“…In addition to the minimum volume upper-bounding ellipsoid , define a family of ellipsoids , where each is tight to along a direction specified by a unitary vector . Note that the can be constructed by solving an optimization program similar to the one in (21). Finally, for all , , and appropriate values of , we obtain ellipsoidal upper bounds on , , , and .…”

Section: Capturing Uncertainty With a Family Of Tight Upper-boundimentioning

confidence: 99%

“…This example examines the preventative maintenance of an electric-power distribution transformer (see [20], [21], and the references therein for a detailed description of this model).…”

Section: B Preventative Transformer Maintenancementioning

confidence: 99%

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A Set-Theoretic Method for Parametric Uncertainty Analysis in Markov Reliability and Reward Models

2013

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This paper proposes a set-theoretic method to capture the effect of parametric uncertainty in reliability and performability indices obtained from Markov reliability and reward models. We assume that model parameters, i.e., component failure and repair rates, are not perfectly known, except for upper and lower bounds obtained from engineering judgment or field data. Thus, the values that these parameters can take are constrained to lie within a set. In our method, we first construct a minimum volume ellipsoid that upper bounds this set, and hence contains all possible values that the parameters can take. This ellipsoid is then propagated via set operations through a second-order Taylor series expansion of the Markov chain stationary distribution, resulting in a set that provides approximate bounds on reliability and performability indices of interest. Case studies pertaining to a two-component shared load system with common-cause failures, and preventative maintenance of an electric-power distribution transformer, are presented.Index Terms-Markov reliability models, Markov reward models, parametric uncertainty, unknown-but-bounded uncertainty models. NOTATIONMarkov chain generator matrix Vector of model parameters Stationary distribution of the Markov chain Vector of nominal model parameters Stationary distribution corresponding to nominal model parameters Jacobian of excluding Manuscript

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Section: Capturing Uncertainty With a Family Of Tight Upper-boundimentioning

confidence: 99%

Section: Capturing Uncertainty With a Family Of Tight Upper-boundimentioning

confidence: 99%

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A Set-Theoretic Method for Parametric Uncertainty Analysis in Markov Reliability and Reward Models

2013

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“…In order to maintain the high-reliability level of the machine during the operational phase, it is necessary to carry out periodic preventive maintenance (Tomasevicz, 2006). Over the course of the analysis, it was determined that the company is carrying out scheduled repairs with a periodicity set by the manufacturer of the equipment, which does not take into consideration all the factors that are encountered during the exploitation of the equipment.…”

Section: Maintenance Schedulingmentioning

confidence: 99%

A reliability-based preventive maintenance methodology for the projection spot welding machine

Ulugbek¹,

Sheng²,

Xiao³

et al. 2018

10.5267/j.msl

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An effective operations of a projection spot welding (PSW) machine is closely related to the effectiveness of the maintenance. Timely maintenance can prevent failures and improve reliability and maintainability of the machine. Therefore, establishing the maintenance frequency for the welding machine is one of the most important tasks for plant engineers. In this regard, reliability analysis of the welding machine can be used to establish preventive maintenance intervals (PMI) and to identify the critical parts of the system. In this reliability and maintainability study, analysis of the PSW machine was carried out. The failure and repair data for analysis were obtained from automobile manufacturing company located in Uzbekistan. The machine was divided into three main sub-systems: electrical, pneumatic and hydraulic. Different distributions functions for all subsystems was tested and their parameters tabulated. Based on estimated parameters of the analyzed distributions, PMI for the PSW machines sub-systems at different reliability levels was calculated. Finally, preventive measures for enhancing the reliability of the PSW machine sub-systems are suggested.

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“…The procedures and steps for using Semi-Markov Processes to model a practical system is shown in Figure 1. The detail description of Semi-Markov processes is given in our prior researches [11].…”

Section: A General Semi-markov Processesmentioning

confidence: 99%

Optimum Maintenance Policy with Inspection by Semi-Markov Decision Processes

Tomasevicz

Asgarpoor

2007

2007 39th North American Power Symposium

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A method which incorporates modeling of equipment deterioration, inspection, and minor & major maintenance for optimal maintenance decision is proposed in this paper. Optimal inspection and maintenance policy to maximize equipment availability and minimize the cost is evaluated by Semi-Markov Processes and Semi-Markov Decision Processes. An example illustrates the implementation of proposed method for maintenance evaluation of circuit breakers. Also the results of how inspection and maintenance rate impact equipment availability and benefit are discussed. This study is valuable for utilities to determine optimal maintenance policy of equipment. 1

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Preventive Maintenance Using Continuous-Time Semi-Markov Processes

Cited by 19 publications

References 6 publications

A Set-Theoretic Method for Parametric Uncertainty Analysis in Markov Reliability and Reward Models

A Set-Theoretic Method for Parametric Uncertainty Analysis in Markov Reliability and Reward Models

A reliability-based preventive maintenance methodology for the projection spot welding machine

Optimum Maintenance Policy with Inspection by Semi-Markov Decision Processes

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