New stochastic models for preventive maintenance and maintenance optimization

Lee, Hyunju; Hwan, Ji

doi:10.1016/j.ejor.2016.04.020

Cited by 73 publications

(68 citation statements)

References 29 publications

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“…Notice that, such repair is not perfect in that they cannot completely restore the system to an as good as new state. Nevertheless, they can completely remove the cumulated hazard rate increment due to external shock damage, eg, …”

Section: Problem Statementmentioning

confidence: 99%

“…Nevertheless, they can completely remove the cumulated hazard rate increment due to external shock damage, eg,. 24 Furthermore, if the system remains operational at nT, then a preventive replacement is immediate to renew the system. The main motivation is to avoid exaggerated failure risks due to the cumulated operational age from the imperfect repair.…”

Section: Maintenance Strategymentioning

confidence: 99%

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Preventive maintenance policy of single‐unit systems based on shot‐noise process

Qiu

Cui

Dong

2018

Quality & Reliability Eng

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This paper develops reliability and maintenance models for a single‐unit system subject to hard failures under random environment of external shocks. Motivated by the observations of shot‐noise process in practice, the impact of shock damage on system failure behavior is characterized by random hazard rate increments. To remove such negative impact, imperfect preventive repair is performed periodically, and preventive replacement is performed after several repairs. Considering the joint effects of both random shocks and imperfect repair on the system hazard rate, we derive recursive equations for the system reliability function. Furthermore, we investigate the optimal maintenance policy that minimizes the expected cost per unit time of the system. The applicability of the reliability and maintenance model is validated by a case study on a wind turbine system.

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Section: Problem Statementmentioning

confidence: 99%

Section: Maintenance Strategymentioning

confidence: 99%

Preventive maintenance policy of single‐unit systems based on shot‐noise process

Qiu

Cui

Dong

2018

Quality & Reliability Eng

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“…In this case, after the repair, the overall reliability level of the system becomes worse. For example, as illustrated in the work of Lee and Cha, malfunction or failure of a component may lead to a more hostile working environment for the other components through increased pressure, temperature, humidity, and so on (see the work of Høyland and Rausand). As an example, in an electric system, a failure of a component may cause electric stress (eg, a sudden electric surge and electric current spikes), which causes some damages on the other components (see the work of Jeong).…”

Section: Introductionmentioning

confidence: 99%

A bivariate optimal replacement policy for a system subject to a generalized failure and repair process

Lee¹,

Hwan

2018

Appl Stoch Models Bus & Ind

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Traditionally, in the studies of the optimal maintenance policies for repairable systems, the nonhomogeneous Poisson process model, which corresponds to the minimal repair process, has been intensively applied. However, in many practical situations, the repair type is not necessarily minimal. In this article, a new repair process based on a new counting process model (so-called the generalized Polya process) is introduced. Then, the issue of the optimal replacement problem is discussed. A bivariate preventive replacement policy is developed and the properties of the optimal policy are studied. Illustrative examples are also presented. In addition, a comparison with a conventional replacement policy is performed.KEYWORDS dependent increments property, generalized Polya process, optimal maintenance policy, stochastic intensity, worse than minimal repair Appl Stochastic Models Bus Ind. 2019;35:637-650.wileyonlinelibrary.com/journal/asmb

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“…The so-called Preventive Maintenance Optimization (PREMO) is based on extensive task analysis rather than system analysis, with a capability to reduce drastically the number of required maintenance tasks in a plant [24][25][26]. Approaches such as PREMO have been very useful to ensure the economical operation of power plants.…”

Section: Literature Reviewmentioning

confidence: 99%

Maintenance Tools applied to Electric Generators to Improve Energy Efficiency and Power Quality of Thermoelectric Power Plants

Fonseca¹,

Bezerra

Leite³

et al. 2017

Energies

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This paper presents a specific method to improve the reliability of the equipment and the quality of power supplied to the electrical systems with the frequency and voltage control of a thermoelectric plant, to guarantee a more stable system. The method has the novelty of combining Total Productive Maintenance (TPM) using only four pillars, with Electrical Predictive Maintenance based in failure analysis and diagnostic. It prevents voltage drops caused by excessive reactive consumption, thus guaranteeing the company a perfect functioning of its equipment and providing a longer life of them. The Maintenance Management Program (MMP) seeks to prevent failures from causing the equipment to be shut down from the electrical system, which means large financial losses, either by reducing billing or by paying fines to the regulatory agency, in addition to prejudice the reliability of the system. Using management tools, but applying only four TPM pillars, it was possible to achieve innovation in power plants with internal combustion engines. This study aims to provide maintenance with a more reliable process, through the implantation of measurement, control and diagnostic devices, thus allowing the management to reduce breakdown of plant equipment. Some results have been achieved after the implementation, such as reduction of annual maintenance cost, reduction of corrective maintenance, increase of MTBF (Mean Time between Failures) and reduction of MTTR (Mean Time to Repair) in all areas. Probabilistic models able to describe real processes in a more realistic way, and facilitate the optimization at maximum reliability or minimum costs are presented. Such results are reflected in more reliable and continual power generation.

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New stochastic models for preventive maintenance and maintenance optimization

Cited by 73 publications

References 29 publications

Preventive maintenance policy of single‐unit systems based on shot‐noise process

Preventive maintenance policy of single‐unit systems based on shot‐noise process

A bivariate optimal replacement policy for a system subject to a generalized failure and repair process

Maintenance Tools applied to Electric Generators to Improve Energy Efficiency and Power Quality of Thermoelectric Power Plants

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