Selective Maintenance Planning Considering Team Capability Based on Fuzzy Integral and Dynamic Artificial Bee Colony Algorithm

Zhang, Lele; Zhang, Libin; Shan, Hongyan; Shan, Hongmei

doi:10.1109/access.2019.2917332

Cited by 6 publications

(3 citation statements)

References 47 publications

(78 reference statements)

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“…The membership matrix R i of machine i is calculated using Eqs. (12), (13), and (14). In the case where the state probability vector…”

Section: Selective Maintenance Optimization For Multistate Manufamentioning

confidence: 99%

“…In order to ensure flexible and cost-effective resource allocation, Yang et al [13] proposed a maintenance strategy for the production waiting process to obey the homogeneous Poisson process. Zhang et al [14] introduced a selective maintenance plan (SMP) that considers the team's maintenance capabilities, and developed a twostage method based on λ fuzzy measure and dynamic multiobjective artificial bee colony (DMABC) for fuzzy Choquet integration to optimize the SMP model.…”

Section: Introductionmentioning

confidence: 99%

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Mission Reliability-Oriented Selective Maintenance Optimization for Intelligent Multistate Manufacturing Systems With Uncertain Maintenance Quality

Chen

Zhao

et al. 2019

IEEE Access

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Selective maintenance is widely used as a reliability-centered maintenance strategy due to the limited maintenance resources. However, existing selective maintenance studies only consider basic reliability, which cannot systematically describe the operating mechanism of a multistate system, thereby resulting in the inability to obtain an optimal maintenance strategy. Moreover, intelligent manufacturing systems are highly representative of typical multistate industrial systems. In this study, a mission reliability-oriented selective maintenance optimization model for intelligent manufacturing systems that considers the uncertain maintenance effect was proposed. First, a new connotation and modeling method for mission reliability based on multistate system theory was presented to comprehensively characterize the operating mechanism of intelligent manufacturing systems. Second, a quantitative model between maintenance resources and quality based on real-time data was established to reflect the uncertain characteristics caused by repairmen and tools. Third, a selective maintenance decision model of a multistate manufacturing system was developed under the constraints of maintenance cost and time. This constraint combination optimization problem was solved using the particle swarm optimization algorithm. Finally, a case study of selective maintenance optimization for a cylinder head manufacturing system was presented to verify the proposed method.

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“…The membership matrix R i of machine i is calculated using Eqs. (12), (13), and (14). In the case where the state probability vector…”

Section: Selective Maintenance Optimization For Multistate Manufamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mission Reliability-Oriented Selective Maintenance Optimization for Intelligent Multistate Manufacturing Systems With Uncertain Maintenance Quality

Chen

Zhao

et al. 2019

IEEE Access

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show abstract

“…Liu et al [26] solved the constrained combinatorial optimization problem through customized ant colony optimization algorithm. Zhang et al [27] proposed a two-phase method integrating fuzzy Choquet integral based on λ-fuzzy measure and dynamic multi-objective artificial bee colony (DMABC) to optimize the SMP models. Bae et al [28] used a neurogenetic methodology to optimize the maintenance reliability allocation of urban transit break system.…”

Section: Introductionmentioning

confidence: 99%

Selective Maintenance Policy for a Series-Parallel System Considering Maintenance Priority of Components

Zhou

Gao

et al. 2020

IEEE Access

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Many systems are required to execute a series of missions with a finite break between any two adjacent missions. In order to improve the reliability of the system completing the next mission successfully, it is necessary to perform maintenance actions on the components considering limited maintenance resources. A novel idea to solve the problem of selective maintenance for a series-parallel system is proposed in this paper. This problem consists in finding the best choice of maintenance actions on a multi-component system by maximizing the system reliability under limited resources. With the increased number of components in the system, the possible combinations of selective maintenance increase exponentially, which raises the difficulty of the problem. For better solution of the problem, from the perspective of maintenance benefit, we develop a selective maintenance policy under maintenance priority of components. Finally, numerical example of a series-parallel system is given to illustrate the proposed method and verify the correctness and accuracy of the method.

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Introduction

Liu

Huang

Jiang

2023

Springer Series in Reliability Engineering

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Selective Maintenance Planning Considering Team Capability Based on Fuzzy Integral and Dynamic Artificial Bee Colony Algorithm

Cited by 6 publications

References 47 publications

Mission Reliability-Oriented Selective Maintenance Optimization for Intelligent Multistate Manufacturing Systems With Uncertain Maintenance Quality

Mission Reliability-Oriented Selective Maintenance Optimization for Intelligent Multistate Manufacturing Systems With Uncertain Maintenance Quality

Selective Maintenance Policy for a Series-Parallel System Considering Maintenance Priority of Components

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