Selective maintenance optimization for fuzzy multi-state systems

Cao, Wenbin; Jia, Xisheng; Liu, Yu; Hu, Qiwei

doi:10.3233/jifs-17031

Cited by 25 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Khatab et al [29][30][31] also investigated a selective maintenance problem for a circumstance when both the durations of the break and the next mission were random variables governed by probability distributions. In addition, Cao et al 32 addressed a selective maintenance optimization problem for a fuzzy multi-state system executing missions with fuzzy mission durations and breaks.…”

Section: Selective Maintenance Optimizationmentioning

confidence: 99%

Selective maintenance optimization for random phased-mission systems subject to random common cause failures

Jia

Cao

2018

Proceedings of the Institution of Mechanical Engineers, Part O:

Self Cite

View full text Add to dashboard Cite

In both industrial and military fields, there is such a kind of complicated system termed as phased-mission system, which executes missions composed of several different phases in sequence. The structure, failure behavior, and working conditions of such a system may change from phase to phase. The duration of each phase of such a system involved is random and follows a probability distribution, and the system may suffer some events resulting in simultaneous failures of different elements with different probabilities. In order to guarantee such a system completes the phased-mission successfully, a selective maintenance model for random phased-mission systems subject to random common cause failures is proposed to optimally identify a subset of maintenance activities to be performed on some elements of the system. Thereinto, a novel analytic model is developed to estimate the probability of the maintained random phased-mission system successfully completing the phased-mission, and we compare it with a well-known Monte Carlo Simulation approach. Finally, the proposed selective maintenance model has been successfully applied to an artillery weapon system. Comparative analysis is carried out to compare the proposed model with the traditional ones, including selective maintenance models for deterministic phased-mission systems and deterministic single-phase mission systems. The results show that ignoring some mission properties (e.g. randomness and multiple phases) in selective maintenance optimization will lead to (1) incorrect system and mission modeling, (2) incorrect computation of the probability of the random phased-mission system successfully completing a mission, and/or (3) nonoptimal selective maintenance options.

show abstract

Section: Selective Maintenance Optimizationmentioning

confidence: 99%

Selective maintenance optimization for random phased-mission systems subject to random common cause failures

Jia

Cao

2018

Proceedings of the Institution of Mechanical Engineers, Part O:

Self Cite

View full text Add to dashboard Cite

show abstract

“…Cassady et al 10 proposed extensions of models that maximize the number of successful missions. In other works, [11][12][13][14][15] an optimization model was proposed that considers a multi-state system (MSS).…”

Section: Maintenance K Mission K Mission K+1mentioning

confidence: 99%

A high‐performance maintenance strategy for stochastic selective maintenance

Zhao

Liu

Zhao

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

Selective maintenance is often applied in many industrial environments where maintenance is performed between sequence missions. When the mission time is stochastic and there are multiple maintenance workers with different capacities, the system reliability of the next work mission can be maximized by using a stochastic model under the constraint of the limit maintenance time.The optimal maintenance strategy is obtained with an optimization algorithm. A simulation was performed to verify the validity and feasibility of the proposed model. KEYWORDS group maintenance, maintenance time, mission time, stochastic model, stochastic selective maintenance INTRODUCTIONAfter the wisdom of China was presented, cyber-physical systems, 1 cloud computing, wireless networks, 2 big data, Vehicular Network, 3,4 Industrial Intelligence Management, etc, have developed rapidly. In the proper and safe operation of industrial and military equipment, transportation systems maintenance is very important. 5 Maintenance management can establish reasonable maintenance policies for complex systems to meet new design requirements with using reliability and maintenance engineering analyses, such as equipment reliability and maintainability, in order to realize timely and effective maintenance to ensure the performance of the system with the lowest resource consumption. 6In many industrial fields (eg, manufacturing systems, military equipment systems, and power generation systems), maintenance is performed between two continuous missions. Each component or equipment can be repaired in different ways, such as no maintenance, minimal maintenance, preventive maintenance, and replacement. However, all maintenance tasks are not fully performed because of limited resources, such as maintenance time, cost, tools, and maintenance workers, 7 so part of the maintenance tasks are executed to maximize the system reliability of the next work mission. This is called selective maintenance. The workflow of selective maintenance is shown in Figure 1.Unlike the deterministic selective maintenance, a feasible deterministic maintenance set cannot be obtained in the stochastic selective maintenance because of the stochastic maintenance time or work time. For different systems, many different models are defined. Cassady et al 7 defined selective maintenance and a mathematical programming model for a two-state system. Cassady et al 8 developed a model that maximizes the system reliability with maintenance time Constraints for a series-parallel system with two-state components. In the model, the lifespan of a component follows a Weibull distribution, so that this reliability function is influenced by the age of components at the beginning of the mission and the mission time. Zhu et al 9 established a time-constrained Concurrency Computat Pract Exper. 2019;31:e4840. wileyonlinelibrary.com/journal/cpe

show abstract

“…Maintenance method include the degree of maintenance and the required amount of resource consumption that can affect maintenance decisions [6]. Most scholars' research on maintenance methods includes imperfect, minimal, and perfect maintenance [7]- [11]. In addition to maintenance and failure costs, Pandey et al [12] considered maintenance break duration and shutdown cost to the selective maintenance model.…”

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

View full text Add to dashboard Cite

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.

show abstract

Selective maintenance optimization for fuzzy multi-state systems

Cited by 25 publications

References 27 publications

Selective maintenance optimization for random phased-mission systems subject to random common cause failures

Selective maintenance optimization for random phased-mission systems subject to random common cause failures

A high‐performance maintenance strategy for stochastic selective maintenance

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

Contact Info

Product

Resources

About