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Data-driven diagnosing for unanticipated fault by a general process model

机译：通过通用过程模型进行数据驱动的意外故障诊断

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摘要

The improvement of unanticipated fault detection and diagnosis (UFDD) capability is a difficult point, and is also a tendency for research and application. In this paper, a general process model (GPM) for unanticipated fault diagnosis is established. And combined with the characteristics of monitoring data, the corresponding diagnosis methods are researched. The model and the methods are used for online unanticipated fault detection, isolation and recognition. The GPM for the unanticipated fault diagnosis is designed, by adopting a three-layer progressive structure, which is comprised of an inherent detection layer (IDL), an unanticipated isolation layer (UIL) and an unanticipated recognition layer (URL). Several key problems in the GPM are analyzed, including the establishment and evaluation of detection statistics, the extraction of fault feature direction, and the design of fault isolation criterion and the calculation of contribution factor. The proposed model and methods are driven by pure data and they can detect and diagnose the unanticipated fault. The proposed approach is evaluated by using an example of a satellite's attitude control system, and excellent results have been obtained.
机译：意外故障检测与诊断（UFDD）能力的提高是一个难点，也是研究和应用的趋势。在本文中，建立了用于意外故障诊断的通用过程模型（GPM）。并结合监测数据的特点，研究了相应的诊断方法。该模型和方法用于在线意外故障检测，隔离和识别。通过采用三层渐进结构来设计用于意外故障诊断的GPM，该结构由一个固有检测层（IDL），一个意外隔离层（UIL）和一个意外识别层（URL）组成。分析了GPM中的几个关键问题，包括检测统计数据的建立和评估，故障特征方向的提取，故障隔离准则的设计和影响因子的计算。所提出的模型和方法是由纯数据驱动的，它们可以检测和诊断意外故障。通过以卫星的姿态控制系统为例对提出的方法进行了评估，并获得了极好的结果。

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