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

机译:基于扩展状态任务网络的多状态制造系统选择性维护决策

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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.
机译:在实际生产中,由于维护成本的限制,无法间歇执行多状态制造系统的最佳维护操作。在这种情况下,选择性维护决策被广泛采用。但是,以前的选择性维护决策仅考虑基本可靠性,而不能完全描述多状态制造系统的运行特性。因此,本文提出了一种选择性维护决策,其目标是最大化多状态制造系统的任务可靠性。首先,定义了选择性维护的新含义,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,提出了扩展状态任务网络,以刻画制造系统的运行特性和任务可靠性模型。第三,在维修成本固定的情况下,推导了基于粒子群优化算法的选择性维修决策方法,使下一次作业的任务可靠性最大化。最后,为了验证所提方法的有效性,给出了多状态气缸盖制造系统选择性维护决策的案例研究。

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