Multiobjective Multistate System Preventive Maintenance Model with Human Reliability

Abstract

Modern equipment is designed to operate under deteriorating performance conditions resulting from internal ageing and/or external environmental impacts influencing downstream maintenance. This study focuses on the development of a multistate system (MSS) that considers a human reliability factor associated with maintenance personnel—a condition-based multiobjective MSS preventive maintenance model (MSSPMM). The study assumes that no more than one maintenance activity is performed to achieve the most appropriate preventive maintenance (PM) strategy and easy implementation and to reduce maintenance error due to human reliability. The MSS performance based on mean system unavailability and total maintenance cost is evaluated using a stochastic model approach, and then, the MSSPMM is used for optimisation. A customised version of the nondominated sorting genetic algorithm III is employed to ensure efficient solution of the PM model with human reliability—which is considered a constrained multiobjective combinatorial optimisation problem. The optimised solutions are determined from the nondominated Pareto frontier comprising the diversified PM alternatives. A helicopter power transmission system is used as an example to illustrate the efficacy and applicability of the proposed approach through sensitivity analyses with relevant parameters.

Condition-Based Multi-State-System Maintenance Models for Smart Grid System with Stochastic Power Supply and Demand

“…The constrained bi-objective PM model primarily addresses the issue of human reliability in implementing the PM strategy, wherein the constraint requirement is a maximum of one PM activity [18] for simple implementation. In this manner, the practicality of the PM strategy is enhanced.…”

“…For preventive maintenance (PM) strategy optimization, Shadmand [17] established a two-objective optimization model for the photovoltaic (PV)-wind hybrid power system, with the objectives of ensuring a suitable equipment cost and overall power system availability. Huang et al [18] established a condition-based multi-objective MSS preventive maintenance model using CTMC to evaluate the MSS performance in terms of the mean system unavailability and total maintenance cost, which considers a human reliability factor associated with maintenance personnel. Chen et al [19] pioneered a novel fuzzy MSS availability using probability theory and uncertainty theory to define the MSS features of random uncertainty, where the state probability and performance of system components are non-probabilistic uncertain variables.…”

“…Considering the scenario that mean power system availability and total maintenance cost are considered of equal importance for smart grid, we successively established two bi-objective PM models with the minimization of the mean power system unavailability and total maintenance cost in parallel with and without optional functional constraint, respectively. The added constraint requires each degradation state of the component to a maximum of one maintenance activity to simplify the implementation of the PM strategy, particularly, if human reliability is considered a concern [18]. Accordingly, two bi-objective PM models can be established using the optional functional constraint as follows:…”

“…A total of 2 18 combinations constitute 2,602,144 possible PMs in the form of distinctive chromosomes with coded binary genes from a combinatorial optimization problem. This inferred result is based on the following.…”

“…The figures indicate that more maintenance activities were conducted for the non-constrained PM model than for the constrained model; thus leading to a higher total maintenance cost than that of the constrained model. The constraint requirement is a maximum of one PM activity due to concerns over human reliability with respect to the application of the PM strategy [18]. For the constrained PM model, with a smart grid with an HPS that requires a mean power system unavailability of less than 0.15, two distinct PM alternatives met the requirement.…”