Optimizing preventive maintenance for mechanical components using genetic algorithms

Tsai, Yuo-Tern; Wang, Kuo‐Shong; Teng, Hwei-Yuan

doi:10.1016/s0951-8320(01)00065-5

Cited by 120 publications

(64 citation statements)

References 21 publications

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“…Assumption 1: The system just have two states, the system will be randomly in a normal operating state or maintenance state [3].…”

Section: Basic Assumptions Of the Modelmentioning

confidence: 99%

Elastic Maintenance Cycle Model of System Based on Multi-objective Particle Swarm Optimization Algorithm

Zhang¹,

Li²

2016

Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science

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Abstract. The failure rate of it will increase with the increase of the system running time, the traditional periodic preventive maintenance can meet system failure characteristics, so improving the safety of gas pipeline and searching the system elastic maintenance period mode have the important practical significance. To improve the reliability of the system, control maintenance costs, according to the change of system failure rate, this paper will combine system reliability with the efficiency of system maintenance, on the theory of preventive maintenance, and use age reduction factor to establish reliability-maintenance cost multi-objective optimization model of the system. Combining with the engineering example, apply the multi-objective particle swarm optimization algorithm to solve the model example, in MATLAB platform.

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“…Assumption 1: The system just have two states, the system will be randomly in a normal operating state or maintenance state [3].…”

Section: Basic Assumptions Of the Modelmentioning

confidence: 99%

Elastic Maintenance Cycle Model of System Based on Multi-objective Particle Swarm Optimization Algorithm

Zhang¹,

Li²

2016

Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science

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“…2.16 introduced by Yuo-Tern Tsai and Kuo-Shong Wan in April 2004 (see [35] , page 91) as a dynamic reliability equation that depicts the degraded behavior of component. The system reliability shows how far we are getting the particular outcome for the given input with as much less wastage as possible.…”

Section: Chapter 2 Component Healthmentioning

confidence: 99%

Solving complex maintenance planning optimization problems using stochastic simulation and multi-criteria fuzzy decision making

Tahvili¹,

Österberg²,

Silvestrov³

et al. 2014

AIP Conference Proceedings

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“…There are a number of QMO techniques in the field of Applied Mathematics and Operational Research, for example, Markov Chains and Analytical hierarchy processes [13]; Genetic Algorithms [14] etc. However, most of the approaches are criticised for being developed for mathematical purposes only and are seldom used in practical asset management to solve real-life maintenance problems [12].…”

Section: Quantitative Maintenance Optimisationmentioning

confidence: 99%

Wind Turbine Maintenance Optimisation: Principles of Quantitative Maintenance Optimisation

2007

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Maintenance optimisation is a crucial issue for industries that utilise physical assets due to its impact on costs, risks and performance. Current quantitative maintenance optimisation techniques include Modelling System Failures MSF (using monte-carlo simulation) and Delay-Time Maintenance Model (DTMM). The MSF investigates equipment failure patterns by using failure distribution, resource availability and spareholdings to determine optimum maintenance requirements. The DTMM approach examines equipment failure patterns by considering failure consequences, inspection costs and the period to determine optimum inspection intervals. This paper discusses the concept, relevance and applicability of the MSF and DTMM techniques to the wind energy industry. Institutional consideration as well as the benefits of practical implementation of the techniques are highlighted and discussed.

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Optimizing preventive maintenance for mechanical components using genetic algorithms

Cited by 120 publications

References 21 publications

Elastic Maintenance Cycle Model of System Based on Multi-objective Particle Swarm Optimization Algorithm

Elastic Maintenance Cycle Model of System Based on Multi-objective Particle Swarm Optimization Algorithm

Solving complex maintenance planning optimization problems using stochastic simulation and multi-criteria fuzzy decision making

Wind Turbine Maintenance Optimisation: Principles of Quantitative Maintenance Optimisation

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