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.

中文翻译：

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具有人类可靠性的多目标多状态系统预防性维护模型

现代设备旨在在由于内部老化和/或影响下游维护的外部环境影响而导致性能恶化的条件下运行。本研究的重点是多状态系统 (MSS) 的开发，该系统考虑了与维护人员相关的人员可靠性因素 - 基于条件的多目标 MSS 预防性维护模型 (MSSPMM)。该研究假设不超过一项维护活动被执行以实现最合适的预防性维护 (PM) 策略和易于实施，并减少由于人为可靠性导致的维护错误。使用随机模型方法评估基于平均系统不可用性和总维护成本的 MSS 性能，然后使用 MSSPMM 进行优化。非支配排序遗传算法 III 的定制版本被用来确保具有人类可靠性的 PM 模型的有效解——这被认为是一个受约束的多目标组合优化问题。优化的解决方案是从包含多样化 PM 替代方案的非支配帕累托边界确定的。以直升机动力传输系统为例，通过相关参数的敏感性分析来说明所提出方法的有效性和适用性。