Selective Maintenance Decision for Multistate Manufacturing System Based on Extended State Task Network

Zhaoxiang Chen, Yihai He, Yixiao Zhao, Xiao Han, Zheng He
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引用次数: 4

Abstract

In actual production, the best maintenance operations of multistate manufacturing system cannot be implemented at intervals due to the limitation of maintenance costs. In this case, selective maintenance decisions are widely adopted. However, previous selective maintenance decisions only consider the basic reliability, which cannot fully describe the operating characteristics of multistate manufacturing system. Therefore, this paper proposes a selective maintenance decision with the goal of maximizing the mission reliability for multistate manufacturing system. Firstly, the new connotation of selective maintenance is defined to characterize the ability of a multistate manufacturing system to meet the variable task demand state. Secondly, the Extended State Task Network is proposed to characterize the operating characteristics of manufacturing system and the mission reliability model. Thirdly, under the condition of fixed maintenance cost, a selective maintenance decision method based on Particle Swarm Optimization algorithm is derived, which makes the mission reliability of next operation to be maximized. Finally, in order to verify the effectiveness of the proposed method, a case study of selective maintenance decision for a multistate cylinder head manufacturing system is given.
基于扩展状态任务网络的多状态制造系统选择性维修决策
在实际生产中,由于维护成本的限制,多状态制造系统的最佳维护操作不能间隔进行。在这种情况下,选择性维护决策被广泛采用。然而,以往的选择性维修决策仅考虑基本可靠性,不能充分描述多状态制造系统的运行特性。因此,针对多状态制造系统,提出了一种以任务可靠性最大化为目标的选择性维修决策方法。首先,定义了选择性维修的新内涵,以表征多状态制造系统满足可变任务需求状态的能力。其次,提出了表征制造系统运行特性的扩展状态任务网络,建立了任务可靠性模型;第三,在维修费用固定的情况下,推导了一种基于粒子群优化算法的选择性维修决策方法,使下次作业的任务可靠性最大化。最后,以多状态气缸盖制造系统的选择性维修决策为例,验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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