A preventive maintenance policy for a pull system with degradation and failures considering product quality

Zhou, Binghai; Cheng, Guoqing; Liu, Ziqiang; Liu, Zilong

doi:10.1177/0954408918784414

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…It is easy to understand and perform. Moreover, the genetic algorithm [40][41][42][43][44] has good global search ability and high search efficiency. Therefore, the genetic algorithm is adopted to solve the above selective maintenance optimization model.…”

Section: Selective Maintenance Optimization Solutionmentioning

confidence: 99%

Selective maintenance of multi-state series systems considering maintenance quality uncertainty and failure effects

Ran

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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In order to improve the lifetime and reliability of equipment operation stage, it is necessary to carry out maintenance for components. The typical selective maintenance model does not consider the maintenance quality uncertainty and the failure effects, which may make the decision-maker overestimates the reliability of completing the next task after system maintenance, resulting in incorrect maintenance decision and prone to serious consequences of failure. In this paper, the potential discrepancy between the target maintenance quality and the actual maintenance quality of the decision maker is considered. At the same time, a multi-state FMECA is proposed to measure the failure effects under different states. Finally, the selective maintenance model of the multi-state series systems is established and solved by the genetic algorithm. The results show that considering the effect of component failure and the uncertainty of maintenance quality has an influence on maintenance decision. The maintenance decision made in this way is more consistent with the engineering practice.

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Section: Selective Maintenance Optimization Solutionmentioning

confidence: 99%

Selective maintenance of multi-state series systems considering maintenance quality uncertainty and failure effects

Ran

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

View full text Add to dashboard Cite

show abstract

“…e grey modeling mechanism based on moving window is shown in Figure 1, where n is the length of original modeling series, N is the number of data points to be predicted, L is the step size of moving window, and a and b represent the coefficients depicted in equation (10).…”

Section: Improved Grey-markov Model With Moving Windowmentioning

confidence: 99%

“…CBM contributes to identify the operational status of equipment and avoid unnecessary downtime maintenance. Because of these advantages, it gradually becomes one of the most commonly used maintenance pattern and attracts more and more focuses of researchers [9,10]. More speci cally, degradation tendency prediction plays an important role in the implementation of CBM, which is helpful to discover abnormal operation states before fault occurs and e ectively decrease the failure rate and maintenance costs [11,12].…”

Section: Introductionmentioning

confidence: 99%

Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model

et al. 2019

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As an essential component and core power source of aircraft, the operational stability of aeroengine has important impact on system safety and reliability. Accurate degradation tendency prediction on an engine can not only improve its operational stability but also significantly reduce the maintenance costs. In this paper, a novel forecasting method that combines CEEMDAN permutation entropy and improved Grey–Markov model is proposed to perform multistep degradation tendency prediction of aircraft engines. In order to accurately quantify the degradation level of engines, a new integrated degradation index (IDI) is innovatively designed by multidimensional sensory data. And then, because of high speed and excellent performance, CEEMDAN algorithm is specifically employed to decompose the generated IDI series to eliminate the potential influence of stochastic fluctuations. Aiming at the complexity of intrinsic mode functions (IMFs) generated by CEEMDAN, an IMFs reconstruction strategy based on permutation entropy is developed to better characterize the degradation states. Finally, on the basis of above achievements and for higher forecasting efficiency and accuracy, an improved Grey–Markov model combined with the moving window algorithm, which is unique, is constructed to realize multistep degradation trend prediction of engines. The proposed method is applied to the degradation tendency prediction of aircraft engines. The experimental results validate the effectiveness and superiority of the proposed method, and it is more suitable for engineering applications in comparison with other methods.

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Lean Manufacturing Origins and Concepts

Díaz-Reza

Alcaráz

García

2022

Best Practices in Lean Manufacturing

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A preventive maintenance policy for a pull system with degradation and failures considering product quality

Cited by 6 publications

References 30 publications

Selective maintenance of multi-state series systems considering maintenance quality uncertainty and failure effects

Selective maintenance of multi-state series systems considering maintenance quality uncertainty and failure effects

Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model

Lean Manufacturing Origins and Concepts

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