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Integrating diagnostic knowledge with nonlinear estimation for comprehensive fault diagnosis.

机译：将诊断知识与非线性估计相结合，以进行全面的故障诊断。

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Faults lead to loss of productivity and can eventually lead to loss of human lives. Therefore, fault diagnosis is a critical procedure for increased reliability and safety requirement. There are a variety of diagnostic techniques studied in literature. Most of these diagnostic methods either focus on parametric faults assuming reliable sensor information or address the various issues in sensor failure without considering other variations in process. The real diagnostic problem is one in which identification of various process and sensor failures is addressed by a single diagnostic system. In this thesis, a framework is presented to address the comprehensive diagnostic problem.; The proposed solution strategy is a hypothesis driven framework which integrates diagnostic techniques with estimation methods. The diagnostic module comes up with explanations for the process abnormality. The hypothesis sets are constructed from the diagnosis module results and are ordered based on apriori information. The estimation module in conjunction with statistical testing is used to evaluate the hypothesis. Various diagnostic techniques based on nonlinear observers, dynamic time warping of qualitative trends and signed directed graphs have been studied. Various estimation methods like Recursive Nonlinear Dynamic Data Reconciliation (RNDDR), Unscented RNDDR and Two-Step Optimization have been explored. A specific implementation of the proposed comprehensive framework for diagnosis of parameter and sensor faults in a Continuous Stirred Tank Reactor (CSTR) has been presented. This implementation uses signed directed graph model of the process for incipient fault diagnosis based on the initial response of the process. The hypothesis sets are evaluated based on principle of parsimony. The Two-Step estimator has been implemented for rapid estimation of the relevant parameters in the hypothesis.
机译：错误会导致生产力下降，并最终导致人员伤亡。因此，故障诊断是提高可靠性和安全性要求的关键过程。文献中研究了多种诊断技术。这些诊断方法中的大多数要么专注于假定传感器信息可靠的参数故障，要么解决传感器故障中的各种问题而无需考虑过程中的其他变化。真正的诊断问题是通过单个诊断系统解决各种过程和传感器故障的识别问题。本文提出了一个解决综合诊断问题的框架。提出的解决方案策略是一个假设驱动的框架，该框架将诊断技术与估计方法相集成。诊断模块会提供有关过程异常的说明。从诊断模块结果构建假设集，并根据先验信息进行排序。估计模块与统计检验一起用于评估假设。已经研究了基于非线性观测器，定性趋势的动态时间扭曲和带符号有向图的各种诊断技术。探索了各种估计方法，例如递归非线性动态数据协调（RNDDR），无味RNDDR和两步优化。提出了一种用于在连续搅拌釜反应器（CSTR）中诊断参数和传感器故障的综合框架的具体实现。此实现基于过程的初始响应，使用过程的带符号有向图模型进行初期故障诊断。假设集基于简约原则进行评估。已经实现了两步估计器，可以快速估计假设中的相关参数。

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