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A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks

机译：使用面向对象的贝叶斯网络的复杂系统实时故障诊断方法

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摘要

Bayesian network (BN) is a commonly used tool in probabilistic reasoning of uncertainty in industrial processes, but it requires modeling of large and complex systems, in situations such as fault diagnosis and reliability evaluation. Motivated by reduction of the overall complexities of BNs for fault diagnosis, and the reporting of faults that immediately occur, a real-time fault diagnosis methodology of complex systems with repetitive structures is proposed using object-oriented Bayesian networks (OOBNs). The modeling methodology consists of two main phases: an off-line OOBN construction phase and an on-line fault diagnosis phase. In the off-line phase, sensor historical data and expert knowledge are collected and processed to determine the faults and symptoms, and OOBN-based fault diagnosis models are developed subsequently. In the on-line phase, operator experience and sensor real-time data are placed in the OOBNs to perform the fault diagnosis. According to engineering experience, the judgment rules are defined to obtain the fault diagnosis results.
机译：贝叶斯网络（BN）是在工业过程中不确定性的概率推理中常用的工具，但是在故障诊断和可靠性评估等情况下，它需要对大型和复杂的系统进行建模。由于降低了用于诊断故障的BN的整体复杂性以及立即报告故障的动机，提出了一种使用面向对象的贝叶斯网络（OOBN）的具有重复结构的复杂系统的实时故障诊断方法。建模方法包括两个主要阶段：离线OOBN构建阶段和在线故障诊断阶段。在离线阶段，将收集传感器历史数据和专家知识并进行处理以确定故障和症状，然后开发基于OOBN的故障诊断模型。在在线阶段，将操作员经验和传感器实时数据放入OOBN中以执行故障诊断。根据工程经验，定义判断规则以获得故障诊断结果。

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  • 来源
    《Mechanical systems and signal processing》 |2016年第12期|31-44|共14页
  • 作者

    Baoping Cai; Hanlin Liu; Min Xie;

  • 作者单位

    College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao, Shandong 266580, China,Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong;

    Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong;

    Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Object-oriented Bayesian networks; Real-time; Fault diagnosis; Complex systems;

    机译：面向对象的贝叶斯网络;即时的;故障诊断;复杂系统;

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