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System dynamic reliability assessment and failure prognostics

机译：系统动态可靠性评估和故障预测

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摘要

Traditionally, equipment reliability assessment is based on failure data from a population of similar equipment, somewhat giving an average description of the reliability performance of an equipment, not capturing the specificity of the individual equipment. Monitored degradation data of the equipment can be used to specify its behavior, rendering dynamic the reliability assessment and the failure prognostics of the equipment, as shown in some recent literature. In this paper, dynamic reliability assessment and failure prognostics with noisy monitored data are developed for a system composed of dependent components. Parallel Monte Carlo simulation and recursive Bayesian method are integrated in the proposed modelling framework to assess the reliability and to predict the Remaining Useful Life (RUL) of the system. The main contribution of the paper is to propose a framework to estimate the degradation state of a system composed of dependent degradation components whose conditions are monitored (even without knowing the initial system degradation state) and to dynamically assess the system risk and RUL. As case study, a subsystem of the residual heat removal system of a nuclear power plant is considered. The results shows the significance of the proposed method for tailored reliability assessment and failure prognostics.
机译：传统上，设备可靠性评估是基于大量类似设备的故障数据，在某种程度上给出了设备可靠性性能的平均描述，而没有捕获单个设备的特殊性。设备的监视退化数据可用于指定其行为，从而动态地进行设备的可靠性评估和故障预测，如最近的一些文献所示。在本文中，针对由相关组件组成的系统，开发了动态可靠性评估和带有嘈杂监视数据的故障预测。并行蒙特卡罗模拟和递归贝叶斯方法被集成在所提出的建模框架中，以评估可靠性并预测系统的剩余使用寿命。本文的主要贡献是提出一个框架，以评估由相关退化组件组成的系统的退化状态，这些组件的状态受到监视（即使不知道初始系统退化状态），并动态评估系统风险和RUL。作为案例研究，考虑了核电站余热去除系统的子系统。结果表明，该方法对于量身定制的可靠性评估和故障预测具有重要意义。

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