Abstract Traditional reliability theory cannot objectively describe the multi-state of the pipeline system with reliability analysis. In addition, studies on reliability modeling of the multi-state pipeline system assume that the states of each pipeline unit are independent. In this work, an efficient method was proposed to identify the performance relationship. Specifically, a model of a multi-state reconstruction system containing the decaying process and the repairing process was established. Also, a multi-state reliability modeling method was developed based on the performance degradation process of the pipeline system considering the failure interaction. Further, the Markov model of the degradation process was built to determine the relationship between the occurrence probability of each state and the decay path. The cloud generalization function was established using cloud inference combined with the general generation function. The pipeline system can be dynamically divided by the analytic hierarchy process according to different factors affecting the degradation of pipeline performance. A numerical example was used to demonstrate the efficiency and applicability of the proposed method by comparing it with existing methods. The proposed approaches are found to be sufficient to improve risk decision and reliability analysis of the pipeline system, which better supports the reliability-informed integrity management for the whole system.

中文翻译：

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基于云推理的故障交互多态重构管道系统可靠性评估

摘要 传统的可靠性理论无法通过可靠性分析客观地描述管道系统的多状态。此外，多状态管道系统可靠性建模的研究假设每个管道单元的状态是独立的。在这项工作中，提出了一种有效的方法来识别性能关系。具体而言，建立了包含衰减过程和修复过程的多状态重建系统模型。此外，基于考虑故障相互作用的管道系统性能退化过程，开发了一种多状态可靠性建模方法。进一步建立退化过程的马尔可夫模型，确定各状态的发生概率与衰减路径的关系。使用云推理结合通用生成函数建立云泛化函数。可以根据影响管道性能退化的不同因素，通过层次分析法对管道系统进行动态划分。通过与现有方法的比较，数值算例证明了所提出方法的有效性和适用性。发现所提出的方法足以改善管道系统的风险决策和可靠性分析，更好地支持整个系统的可靠性通知完整性管理。可以根据影响管道性能退化的不同因素，通过层次分析法对管道系统进行动态划分。通过与现有方法的比较，数值算例证明了所提出方法的有效性和适用性。发现所提出的方法足以改善管道系统的风险决策和可靠性分析，更好地支持整个系统的可靠性通知完整性管理。可以根据影响管道性能退化的不同因素，通过层次分析法对管道系统进行动态划分。通过与现有方法的比较，数值算例证明了所提出方法的有效性和适用性。发现所提出的方法足以改善管道系统的风险决策和可靠性分析，更好地支持整个系统的可靠性通知完整性管理。