Selective maintenance for binary systems under imperfect repair

Pandey, Mayank; Zuo, Ming J.; Moghaddass, Ramin; Tiwari, Manoj Kumar

doi:10.1016/j.ress.2012.12.009

Cited by 141 publications

(109 citation statements)

References 18 publications

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“…En un sistema, los componentes interactúan entre sí para llevar a cabo funciones operacionales específicas [22], por lo tanto, la condición del sistema en general, depende de la condición de sus componentes [1]. Es importante entonces conocer la configuración del sistema para comprender el comportamiento del mismo ante las fallas.…”

Section: Configuración Del Sistema Y Reglas De Operaciónunclassified

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Modelo de toma de decisiones de mantenimiento basado en la predicción de vida útil para componentes de sistemas eólicos en Costa Rica

Loría-García

Villalobos-Granados

Piedra-Santamaría

2017

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Section: Configuración Del Sistema Y Reglas De Operaciónunclassified

“…La configuración del sistema describe la manera en la cual el sistema se encuentra conectado, así como sus reglas de operación [22]. Para evaluar la confiabilidad de sistemas, estos suelen descomponerse jerárquicamente de acuerdo con el desempeño de los componentes.…”

Section: Configuración Del Sistemaunclassified

Modelo de toma de decisiones de mantenimiento basado en la predicción de vida útil para componentes de sistemas eólicos en Costa Rica

Loría-García

Villalobos-Granados

Piedra-Santamaría

2017

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“…The same imperfect selective maintenance model was studied in Zhu et al (2011) and applied to a machining line system in the automotive industry. Panday et al (2013) also studied the selective maintenance problem for binary systems under imperfect maintenance using the hybrid hazard rate approach introduced in Lin, Zuo, and Yam (2000). In Liu and Huang (2010) and Panday et al (2013), a set of maintenance levels, ranging from minimal repair to replacement, are used to improve the reliability of a system component.…”

Section: Introductionmentioning

confidence: 99%

“…Panday et al (2013) also studied the selective maintenance problem for binary systems under imperfect maintenance using the hybrid hazard rate approach introduced in Lin, Zuo, and Yam (2000). In Liu and Huang (2010) and Panday et al (2013), a set of maintenance levels, ranging from minimal repair to replacement, are used to improve the reliability of a system component. Note that in Liu and Huang (2010) and Zhu et al (2011), the only parameter that determines the improvement in the component's health is the age reduction coefficient.…”

Section: Introductionmentioning

confidence: 99%

“…Note that in Liu and Huang (2010) and Zhu et al (2011), the only parameter that determines the improvement in the component's health is the age reduction coefficient. In Panday et al (2013), however, both the age reduction coefficient and the adjustment coefficient impact the component's health. A more recent work, studied the selective maintenance problem when the quality of imperfect maintenance is stochastic.…”

Section: Introductionmentioning

confidence: 99%

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Selective maintenance optimisation for series-parallel systems alternating missions and scheduled breaks with stochastic durations

Khatab¹,

Aghezzaf

Diallo

et al. 2017

International Journal of Production Research

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This paper deals with the selective maintenance problem for a multi-component system performing consecutive missions separated by scheduled breaks. To increase the probability of successfully completing its next mission, the system components are maintained during the break. A list of potential imperfect maintenance actions on each component, ranging from minimal repair to replacement is available. The general hybrid hazard rate approach is used to model the reliability improvement of the system components. Durations of the maintenance actions, the mission and the breaks are stochastic with known probability distributions. The resulting optimisation problem is modelled as a non-linear stochastic programme. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the components given the limited stochastic duration of the break and the minimum system reliability level required to complete the next mission. The fundamental concepts and relevant parameters of this decision-making problem are developed and discussed. Numerical experiments are provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimisation programme.

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A high‐performance maintenance strategy for stochastic selective maintenance

Zhao

Liu

Zhao

et al. 2018

Concurrency and Computation

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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

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Selective maintenance for binary systems under imperfect repair

Cited by 141 publications

References 18 publications

Modelo de toma de decisiones de mantenimiento basado en la predicción de vida útil para componentes de sistemas eólicos en Costa Rica

Modelo de toma de decisiones de mantenimiento basado en la predicción de vida útil para componentes de sistemas eólicos en Costa Rica

Selective maintenance optimisation for series-parallel systems alternating missions and scheduled breaks with stochastic durations

A high‐performance maintenance strategy for stochastic selective maintenance

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