Joint Optimization of Reliability Design and Level of Repair Analysis Considering Time Dependent Failure Rate of Fleet System

doi:10.23940/ijpe.20.06.p1.821833

Abstract

Abstract: This paper presents a joint optimization approach of reliability design (RD) and level of repair analysis (LORA) for fleet systems. A fleet is a multi-machine, multi-indenture system. The present paper investigates the interrelated effect of product design in terms of modularity and inherent reliability with maintenance repair strategy. It proposes a joint approach for the configured fleet system design of reliability and repair decisions at the initial design phase considering the time dependent failure rate of components. The consequences of each design options and level of repair decisions are evaluated based on life cycle cost performance. Additionally, the failure of the machine is modeled using time dependent failure rate models at the part level of the indenture. The methodology integrates many interdependent maintenance decisions such as location of maintenance, type of maintenance (repair/replacement/discard), and indenture level at which maintenance should be performed. The time dependent failure rate of components provides flexibility to consider the effect of practical behavior on the overall fleet level maintenance methodology. This makes the fleet methodology more realistic in terms of optimizing the reliability design and maintenance repair decisions (LORA). This joint problem is the complex combinatorial type problem. To obtain appropriate integrated and disintegrated results for fleet system design and level of repair decisions, genetic algorithm (GA)-based Monte Carlo simulation is used.

Key words: reliability design, level of repair analysis, life cycle cost, genetic algorithm, simulation and optimization

Manish Rawat and Bhupesh Kumar Lad. Joint Optimization of Reliability Design and Level of Repair Analysis Considering Time Dependent Failure Rate of Fleet System [J]. Int J Performability Eng, 2020, 16(6): 821-833.

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