Joint optimal maintenance and inspection for a k-out-of-n system

Babishin, Vladimir; Taghipour, Sharareh

doi:10.1007/s00170-016-8570-z

Cited by 32 publications

(15 citation statements)

References 20 publications

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“…In preference to previous works (e.g. see [8,9,11,13,17,23,[25][26][27][28][29][31][32][33]), both the cost structure and the inspection model explored here respond to the variation of the system state. Examples of papers that use particular cost structures can be found in [11,23,26,33].…”

Section: Related Workmentioning

confidence: 99%

“…Maintenance optimization models based on the periodic inspection policy is vast (e.g. see [8,9,17,18,25,28,29]). For example, Lienhardt [17] study both the corrective maintenance and a periodic inspection policy for a repairable system subject to non-self announcing failures.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, based on a periodic inspection policy, Liu et al [18] develop a delay-time-based model to determine the optimal inspection interval for parallel systems. More recently, Bjarnason and Taghipour [8] investigate periodic inspection frequency and inventory policies for a k-out-of-n system where the component's failures are hidden and conform to a non-homogeneous Poisson process.…”

Section: Related Workmentioning

confidence: 99%

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A novel data-driven approach to optimizing replacement policy

Ahmadi

2017

Reliability Engineering & System Safety

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Parallel systems are a commonly used structure in reliability engineering. A common characteristic of such systems is that the failure of a component may not cause its system to fail. As such, the failure may not be immediately detected and the random (disruption) time at which the number of failed components reaches a certain predefined number d may therefore be unknown. For such systems, scheduling maintenance policy is a difficult task, which is tackled in this paper. The paper assumes that times between inspections conform to a modulated Poisson process. This assumption allows the frequency of inspection responds to the variation of the disruption state. The paper then estimates the disruption time on the basis of inspection point process observations in the framework of filtering theorem. The paper develops a unified cost structure to jointly optimise inspection frequency and replacement time for the system when the lifetime distribution of a component follows the Pareto or exponential distribution. Numerical results are provided to show the application of the proposed model. Nomenclature mNumber of components in the system t r Periodic replacement time X d (t) Disruption state τ d Disruption (alert) time at which the process X d (t) describing the system state jumps from normal state (0) to the degraded state (1)

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A novel data-driven approach to optimizing replacement policy

Ahmadi

2017

Reliability Engineering & System Safety

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“…Babishin and Taghipour [86] propose a joint optimisation procedure for minimising the total cost of both periodic and opportunistic inspections and corrective maintenance for a k-out-of-n system with hidden failures. In another article, Babishin and Taghipour [87] consider a k-out-of-n system with hidden failures under both periodic and opportunistic inspections, minimal repair, corrective or preventive replacement and find the optimal joint inspection and maintenance policies, as well as provide expressions for the expected number of system failures in terms of the cost ratio and component failure intensity. They also derive a criterion for calculating the acceptable number of system failures over the system's life cycle.…”

Section: Introductionmentioning

confidence: 99%

“…Genetic algorithms (GA) have been used in the literature for inspection optimisation of multicomponent systems (Moghaddam and Usher [119], Alrabghi and Tiwari [120], Moura et al [121], Gen and Cheng [122], Goldberg [123]). Due to the lack of a closed-form solution, joint optimisation is performed using exhaustive search and genetic algorithm with either integer, or quasi-continuous inspection period by Babishin and Taghipour [87]. They provide the optimal joint inspection and maintenance policies, as well as expressions for the expected number of system failures in terms of the cost ratio and component failure intensity for a k-outof-n system with hidden failures under both periodic and opportunistic inspections, minimal repair, corrective or preventive replacement.…”

Section: Introductionmentioning

confidence: 99%

Inspection and maintenance optimisation of multicomponent systems

Babishin¹

2021

Preprint

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The present research proposes methodology and mathematical models for optimisation of inspection and maintenance in complex multicomponent systems with finite planning horizon. Components are classified by failure types: hard-type and soft-type. The systems analysed are composed of either multiple identical hidden soft-type components in k-out-of-n redundant configuration, or a combination of hard-type and hidden soft-type components. Failures of hard-type components cause system failures. Failures of components in k-out-of-n systems and soft-type component failures are hidden and not discoverable until an inspection, but reduce the system’s reliability and performance. The systems are inspected either periodically, or non-periodically. They are also inspected opportunistically at the times of system failure (occurring at (k – n + 1)st component failures in k-out-of-n systems, or at hard failures in the systems composed of hard-type and soft-type components). Inspections have negligible duration. All components may undergo minimal repair, or corrective replacement, with hard-type components also having a possibility of preventive replacement under periodic inspections. We only consider minimal repair and corrective replacement under non-periodic inspections. We propose several models for joint optimisation of inspection and maintenance policies that result in the lowest total expected cost. Since soft failures are hidden, we generate expected values for the number of minimal repairs, number of replacements and downtime recursively. Due to multiple component interactions and system complexity, Monte Carlo simulation and genetic algorithms (GA) are used for optimisation. Using GA for optimisation allows to consider quasi-continuous inspection intervals due to improved computational efficiency compared to Monte Carlo simulation. Some of proposed models feature preventive component replacements and are applicable even for systems with hidden component failures. For k-out-of-n systems, we apply periodic model to series and parallel systems and compare the results. We provide expressions for expected number of system failures in terms of cost ratio and component failure intensity. We also provide a simplified expression for system reliability. In addition, we derive a formula for finding the planning horizon length based on expected number of system failures. It may be useful for planning the system’s operating horizon, at the system design stage and when analysing its performance.

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