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首页> 外文期刊>Mechanical systems and signal processing >Condition monitoring of distributed systems using two-stage Bayesian inference data fusion
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Condition monitoring of distributed systems using two-stage Bayesian inference data fusion

机译：使用两阶段贝叶斯推理数据融合的分布式系统状态监测

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摘要

In industrial practice, condition monitoring is typically applied to critical machinery. A particular piece of machinery may have its own condition monitoring system that allows the health condition of said piece of equipment to be assessed independently of any connected assets. However, industrial machines are typically complex sets of components that continuously interact with one another. In some cases, dynamics resulting from the inception and development of a fault can propagate between individual components. For example, a fault in one component may lead to an increased vibration level in both the faulty component, as well as in connected healthy components. In such cases, a condition monitoring system focusing on a specific element in a connected set of components may either incorrectly indicate a fault, or conversely, a fault might be missed or masked due to the interaction of a piece of equipment with neighboring machines. In such cases, a more holistic condition monitoring approach that can not only account for such interactions, but utilize them to provide a more complete and definitive diagnostic picture of the health of the machinery is highly desirable. In this paper, a Two-Stage Bayesian Inference approach allowing data from separate condition monitoring systems to be combined is presented. Data from distributed condition monitoring systems are combined in two stages, the first data fusion occurring at a local, or component, level, and the second fusion combining data at a global level. Data obtained from an experimental rig consisting of an electric motor, two gearboxes, and a load, operating under a range of different fault conditions is used to illustrate the efficacy of the method at pinpointing the root cause of a problem. The obtained results suggest that the approach is adept at refining the diagnostic information obtained from each of the different machine components monitored, therefore improving the reliability of the health assessment of each individual element, as well as the entire piece of machinery.
机译：在工业实践中，状态监视通常应用于关键机械。特定的机器可以具有自己的状态监视系统，该系统可以独立于任何关联资产来评估该设备的健康状况。但是，工业机械通常是一组复杂的组件，这些组件不断相互交互。在某些情况下，故障产生和发展所产生的动力会在各个组件之间传播。例如，一个组件中的故障可能导致有故障的组件以及连接的健康组件中的振动水平增加。在这种情况下，专注于一组连接的组件中的特定元素的状态监视系统可能会错误地指示故障，或者相反，由于一件设备与相邻机器的交互作用，可能会漏掉或掩盖故障。在这种情况下，非常需要一种不仅要考虑这种相互作用，而且要利用它们来提供更完整，更明确的机械运行状况诊断图的整体状态监视方法。在本文中，提出了一种两阶段贝叶斯推理方法，该方法允许将来自不同状态监视系统的数据进行组合。来自分布式状态监视系统的数据分为两个阶段，第一个数据融合发生在本地或组件级别，第二个融合合并数据在全局级别。从包括电动机，两个齿轮箱和一个负载的实验装置获得的数据在一系列不同的故障条件下运行，这些数据说明了该方法在查明问题根本原因方面的功效。获得的结果表明，该方法擅长完善从所监视的每个不同机器组件中获得的诊断信息，从而提高了每个单独元件以及整个机器的健康状况评估的可靠性。

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  • 来源
    《Mechanical systems and signal processing》 |2017年第ptaa期|91-110|共20页
  • 作者

    Victor H. Jaramillo; James R. Ottewill; Rafal Dudek; Dariusz Lepiarczyk; Pawel Pawlik;

  • 作者单位

    ABB Corporate Research Center, ul. Starowislna 13A, 31-038 Krakow, Poland,Universidad EIA, Mechatronics Engineering Department, Km 2+200 Via al Aeropuerto JMC, 055428 Envigado, Colombia;

    ABB Corporate Research Center, ul. Starowislna 13A, 31-038 Krakow, Poland;

    AGH University of Science and Technology, Faculty of Mechanical Engineering and Robotics, Department of Machine Design and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland;

    AGH University of Science and Technology, Faculty of Mechanical Engineering and Robotics, Department of Machine Design and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland;

    AGH University of Science and Technology, Faculty of Mechanical Engineering and Robotics, Department of Mechanics and Vibroacoustics, al. A. Mickiewicza 30, 30-059 Krakow, Poland;

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  • 正文语种 eng
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  • 关键词

    Data fusion; Condition monitoring; Fault diagnosis; Bayesian inference;

    机译：数据融合;状态监测;故障诊断;贝叶斯推断;

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