Adaptive sequential predictive maintenance policy with nonperiodic inspection for hard failures

Wu, Lang; Qiang, Zexuan

doi:10.1002/qre.2788

Cited by 11 publications

(9 citation statements)

References 25 publications

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“…Usually, redundancy is composed of parallel units in each subsystem, therefore, the reliability R i of the i th subsystem can be given as follows (6):…”

Section: Ta B L Ementioning

confidence: 99%

“…Reliability optimization usually includes reliability redundancy allocation optimization and reliability allocation optimization 6,7 . Reliability redundancy allocation refers to determining the corresponding redundancy quantity for each unit when the reliability index of each unit in the system is known, so as to minimize the production cost on the premise of meeting the overall reliability index of the system.…”

Section: Introductionmentioning

confidence: 99%

“…Reliability optimization usually includes reliability redundancy allocation optimization and reliability allocation optimization. 6,7 Reliability redundancy allocation refers to determining the corresponding redundancy quantity for each unit when the reliability index of each unit in the system is known, so as to minimize the production cost on the premise of meeting the overall reliability index of the system. Reliability allocation refers to assigning corresponding reliability indexes to each unit on the basis of comprehensively considering the production cost of each unit in the system, so as to obtain the minimum production cost on the premise of meeting the overall reliability index of the system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Zheng

Zhang

et al. 2023

Quality & Reliability Eng

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In order to improve the global optimization of particle swarm optimization (PSO), enhance the performance of PSO in dealing with these complex, highdimensional, multimodal optimization problems, and furthermore, promote the optimization effect in reliability optimization applications, a direct position updating-based trying-mutation PSO (DTPSO) was proposed. In this algorithm, the direct position updating strategy and trying-mutation strategy were designed, which could effectively maintain the population diversity, balance the exploitation and exploration, and increase the probability of obtaining global optimal solution. After being verified and compared by nine complex test functions, the rationality of algorithm design and the excellent global optimization performances were proved. In the reliability optimization, the reliability redundancy allocation and reliability allocation were optimized by different advanced improved algorithms, the comparison results proved the stability and optimization performance of DTPSO. Due to its stable and efficient global optimization performance, it was concluded the proposed DTPSO could provide effective technical support for practical applications such as reliability optimization.

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“…Usually, redundancy is composed of parallel units in each subsystem, therefore, the reliability R i of the i th subsystem can be given as follows (6):…”

Section: Ta B L Ementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Zheng

Zhang

et al. 2023

Quality & Reliability Eng

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“…In practice, downtimes of equipment and systems are costly 15,16 . Preventive maintenance can prevent equipment from serious failures with long downtimes 17–19 .…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal maintenance period and maintenance sequence planning under imperfect maintenance

Zhang

Jiang

Zheng

et al. 2022

Quality & Reliability Eng

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Maintenance is essential for today's equipment or systems and maintenance costs account for a significant portion of the overall expenditure of devices. Maintenance period optimization and maintenance sequence planning are primary aspects of maintainability. To this end, this paper proposes an approach for maintenance period optimization and maintenance sequence planning, with the consideration of imperfect maintenance. A model of the total maintenance cost per unit time is set up, and the optimal time period for preventive maintenance is calculated. In addition, a new index is created to evaluate the maintenance efficiency of optimal maintenance sequences in different scenarios. The performance of the proposed method is validated by a maintenance sequence optimization and maintenance sequence planning of baggage handling systems. The results indicate that the proposed method is efficient and contributes to maintenance cost savings.

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“…Another set of models studied in optimal maintenance literature, see for instance [8], considers also inspections of actual system state. Then the maintenance is optimized in a sequential manner conditionally on the current prediction of system reliability.…”

Section: P Volfmentioning

confidence: 99%

Application of Stochastic Optimization to Optimal Preventive Maintenance Problem

Volf¹

2021

JAMP

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The contribution deals with the optimization of a sequential preventive maintenance schedule of a technical device. We are given an initial time-tofailure probability distribution, model of changes of this distribution after maintenance actions, as well as the costs of maintenance, of a device acquisition, and of the impact of failure. The maintenance timing and, eventually, its extent, are the matter of optimization. The objective of the contribution is two-fold: first, to formulate a proper (random) objective function evaluating the lifetime of the maintained device relatively to maintenance costs; second, to propose a numerical method searching for a maintenance policy optimizing selected characteristics of this objective function. The method is based on the MCMC random search combined with simulated annealing. It is also shown that such a method is rather universal for different problem specifications. The approach will be illustrated on an artificial example dealing with accelerated lifetime after each maintenance action.

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Adaptive sequential predictive maintenance policy with nonperiodic inspection for hard failures

Cited by 11 publications

References 25 publications

Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Optimal maintenance period and maintenance sequence planning under imperfect maintenance

Application of Stochastic Optimization to Optimal Preventive Maintenance Problem

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