Scheduling heterogeneous repair channels in selective maintenance of multi-state systems with maintenance duration uncertainty

Yin, Mingang; Liu, Yu; Liu, Shuntao; Chen, Yiming; Yi, Yan

doi:10.1016/j.ress.2022.108977

Cited by 22 publications

(5 citation statements)

References 50 publications

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“…Integrating corrective replacement, minimum mainte-nance, and preventive replacement, the selective maintenance model for Weibull distributed components was investigated by Cassady et al [9]. and Xia et al [10] explored a model of selective maintenance for series-parallel systems by considering a combination of corrective maintenance, preventive maintenance, and preventive replacement. Yin et al [11] considered the imperfect maintenance strategies and examined the problem of selective maintenance of multiple repair channels.…”

Section: Relevant Literaturementioning

confidence: 99%

Selective maintenance decision optimization for systems executing multi-mission under stochastic mission duration

Weining,

Enzhi,

Hua

et al. 2024

J. of Syst. Eng. Electron.

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This paper investigates the selective maintenance of systems that perform multi-mission in succession. Selective maintenance is performed on systems with limited break time to improve the success of the next mission. In general, the duration of the mission is stochastic. However, existing studies rarely take into account system availability and the repairpersons with different skill levels. To solve this problem, a new multi-mission selective maintenance and repairpersons assignment model with stochastic duration of the mission are developed. To maximize the minimum phase-mission reliability while meeting the minimum system availability, the model is transformed into an optimization problem subject to limited maintenance resources. The optimization is then realized using an analytical method based on a self-programming function and a Monte Carlo simulation method, respectively. Finally, the validity of the model and solution method approaches are verified by numerical arithmetic examples. Comparative and sensitivity analyses are made to provide proven recommendations for decision-makers.

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Section: Relevant Literaturementioning

confidence: 99%

Selective maintenance decision optimization for systems executing multi-mission under stochastic mission duration

Weining,

Enzhi,

Hua

et al. 2024

J. of Syst. Eng. Electron.

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“…With uncertainty being considered, a selective maintenance model for MSSs was established where the maintenance durations were stochastic [22]. By taking account of the uncertainties associated with the time durations of maintenance tasks and breaks, a new selective maintenance model with multiple heterogeneous repair channels was formulated [23]. A selective maintenance and repairpersons assignment model was developed, in which the durations of missions, maintenance actions, and breaks are stochastic [24].…”

Section: Introductionmentioning

confidence: 99%

Selective Maintenance for a Multistate System Considering Energy Loss and Environmental Effect

Sun,

Zhou,

Sun

et al. 2024

IEEE Access

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With the increasing energy loss and environmental pollution caused by engineering systems, energy conservation and environmental protection have received more attention in industry environments. Based on this consideration, this paper introduces energy loss and environmental effect into the selective maintenance for a multistate system. According to the relationship between energy loss and system degradation, as well as the relationship between environmental pollutant discharge and system degradation, the energy loss and environmental effect models are proposed, respectively. With these models used as a basis, the selective maintenance model for a multistate system that considers energy loss and environmental effect is constructed, and a differential evolution algorithm is employed to solve the resulting maintenance strategy optimization problem. Finally, with a coal transportation system taken as an example, the energy loss and environmental effect models proposed in this paper are verified by comparison with the Monte-Carlo simulation method. Based on this, analyses of the selective maintenance strategy optimization are carried out to demonstrate the effectiveness of the proposed selective maintenance model and optimization method. The analysis results have engineering significance for how to introduce energy loss and environmental effect into the selective maintenance strategy optimization for a multistate system.

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“…Zhang et al [15] used a Markov process to divide multi-state systems into degradation spaces and make maintenance decisions. Yin et al [16] proposed a selective maintenance model with multiple heterogeneous repair channels for multi-state coal transport systems. Zhang et al [17] presented a heuristic maintenance scheduling framework for military aircraft fleets with limited maintenance capabilities.…”

Section: Introductionmentioning

confidence: 99%

Degradation modeling and remaining life prediction of multi-state long-life systems under random environmental influences

Sun,

Shen,

Zhang

et al. 2024

Meas. Sci. Technol.

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For large-scale systems such as bridges, which have long operating lifetimes, the operating states are usually categorized into multiple levels, and they are also subjected to various random environmental influences during operation. However, due to the significant granularity in the categorization of system states, it is difficult to assess the system state transitions influenced by random environmental factors, which compromises the accuracy of remaining life predictions. In this study, we focus on long-life systems with multiple states and investigate the degradation modeling and remaining life prediction considering the impact of random environmental factors. The system degradation process, based on the semi-Markov process and multi-state modeling, was decomposed into states using the sub-exponential approximation method. A state transition probability model considering exponential environmental influences was constructed. Furthermore, based on the developed model for calculating the distribution of sojourn times in multiple states, a reliability and remaining life prediction model for the system was derived. By taking the bridge deck as a case study, the verification and analysis of remaining life prediction for the bridge deck were conducted under the influences of average daily traffic volume and bridge age. The results indicate that both the average daily traffic volume and bridge age have a significant impact on the degradation of the bridge deck. The relative error of the predicted results considering the above effects falls within the range of 1.77%–12.18%.

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Scheduling heterogeneous repair channels in selective maintenance of multi-state systems with maintenance duration uncertainty

Cited by 22 publications

References 50 publications

Selective maintenance decision optimization for systems executing multi-mission under stochastic mission duration

Selective maintenance decision optimization for systems executing multi-mission under stochastic mission duration

Selective Maintenance for a Multistate System Considering Energy Loss and Environmental Effect

Degradation modeling and remaining life prediction of multi-state long-life systems under random environmental influences

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