Research on Improvement and Applications for Bayesian Fault Diagnosis

控制回路故障检测与诊断有助于保证生产过程的安全和高效、降低维护费用和减少停机时间。贝叶斯诊断是控制回路监测的概率化诊断框架，它能够综合多个监测器技术，以构建诊断系统进而作出最优决策。然而，工业过程控制回路诊断中存在许多不同的实际情况，严重制约了贝叶斯诊断的性能。本文重点从数据降维、似然估计等方面研究改进贝叶斯诊断性能的方法，提出了基于优化直方图估计的证据离散化方法、基于线性判别分析的特征提取与降维以及平均移动似然估计方法。通过仿真系统、工业基准数据和工业规模系统的仿真实验，验证了所提方法的有效性。论文主要包含以下几个方面的工作： (1) 综述了现有的贝叶斯诊断方法及其研究现状，系统介绍了控制...The purpose of control loop detection and diagnosis is to ensure the safety and efficacy of the production process, reduce maintenance costs and downtime. Bayesian diagnosis is a probabilistic diagnosis framework of control loop monitoring, which can combine multiple monitor technology to build a diagnosis system and make an optimal decision. However, there are many different situations in the con...学位：工程硕士院系专业：航空航天学院_工程硕士(控制工程)学号：2322013115337

Research on Control Loop Fault Diagnosis

控制回路故障诊断旨在提高工业生产装置操作的安全性与可靠性，一直受到学术界与工业界的广泛关注。工业过程控制回路涉及多种复杂问题，其故障可能存在多种形式。论文研究控制回路中模型预测控制（MPC）的模型失配故障、回路振荡故障，以及基于数据驱动的贝叶斯故障诊断。针对工业现场的实际问题，提出了外加测试信号的模型误差诊断，基于频域分析的回路振荡监测，和基于期望极大化（EM）算法的贝叶斯故障诊断方法。本文具体研究内容如下： 针对MPC模型失配问题，提出了基于外加低幅正弦测试信号的模型诊断方法。通过获取过程三个频率点上的精确频率响应并与当前MPC模型比较，加权形成模型误差矩阵；给出模型诊断误差上界概念，估计...Control loop fault diagnosis deals with the safety and consistency of control loop operation, thus receiving increasing attention in both academic research and industrial application. Since process control systems are complex, usually faults may occur in different components and represent in different behaviors. This thesis is concerned with different faults related to control system, which includ...学位：工学博士院系专业：信息科学与技术学院_控制理论与控制工程学号：2322011015411

Bayesian diagnostics of structural equation models.

行为学、社会学、心理学和医药学方面，结构方程模型(SEMs) 是研究有关潜在变量最常用的模型。这篇论文的目的是研究基本和高级结构方程模型的贝叶斯诊断，本文研究的结构方程模型包括非线性纺构方程模型、变换结构方程模型、二层结构方程模型和混合结构方程模型。基于对数贝叶斯因子的一阶与二阶局部影响测度是本文进行贝贝叶斯诊断的基础。局部影响测度的计算和模型参数估计是利用了蒙特卡洛(MCMC) 和扩展数据的方法。对比传统的基于极大似然的诊断，本文提出的贝叶斯诊断方法不仅能检测异常点或者影响点，而且可以诊断模型假设和先验设定的敏感性。 这些是通过对数据、模型假设和先验设定进行不同的扰动获得的 本文用大量的模拟实验来说明所提出的贝叶斯诊断方法的作用。 本文基于不同类型的结构方程模型，应用所提出的贝叶斯诊断方法于一些实际数据。In the behavioral, social, psychological, and medical sciences, the most widely used models in assessing latent variables are structural equation models (SEMs). This thesis aims to develop Bayesian diagnostic procedures for basic and advanced SEMs such as nonlinear SEMs, transformation SEMs, two-level SEMs, and mixture SEMs. The first- and second-order local inference measures with the objective functions defined based on the logarithm of Bayes factor are proposed to perform the Bayesian diagnostics. Markov chain Monte Carlo (MCMC) methods, along with data augmentation, are developed to compute the local influence measures and to estimate unknown model parameters. Compared with conventional maximum likelihood-based diagnostic procedures, the proposed Bayesian diagnostic approach can not only detect outliers or influential points in the observed data, but also conduct model comparison and sensitivity analysis by perturbing the data, sampling distributions, and the prior distributions of model parameters via a variety of perturbations. The empirical performances of the proposed Bayesian diagnostic procedures are revealed through extensive simulation studies. Several real-life data sets are used to illustrate the application of our proposed methodology in the context of different SEMs.Detailed summary in vernacular field only.Chen, Ji.Thesis (Ph.D.)--Chinese University of Hong Kong, 2013.Includes bibliographical references (leaves 130-135).Abstract also in Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Structural equation models --- p.1Chapter 1.2 --- Bayesian diagnostics --- p.3Chapter 1.2.1 --- The first and second order local influence measures --- p.5Chapter 1.2.2 --- A simple example --- p.9Chapter 2 --- Bayesian diagnostics of nonlinear SEMs --- p.15Chapter 2.1 --- Model description --- p.16Chapter 2.2 --- Bayesian estimation and local inference of nonlinear SEMs --- p.17Chapter 2.3 --- Simulation study --- p.24Chapter 2.3.1 --- Simulation study 1 --- p.24Chapter 2.3.2 --- Simulation study 2 --- p.25Chapter 2.3.3 --- Simulation study 3 --- p.27Chapter 2.4 --- Application: A study of kidney disease for type 2 diabetic patients --- p.29Chapter 3 --- Bayesian diagnostics of transformation SEMs --- p.40Chapter 3.1 --- Model description --- p.41Chapter 3.2 --- Bayesian estimation and local inference of the transformation SEMs --- p.44Chapter 3.3 --- Simulation study --- p.54Chapter 3.3.1 --- Simulation study 1 --- p.54Chapter 3.3.2 --- Simulation study 2 --- p.56Chapter 3.4 --- Application: A study on the risk factors of osteoporotic fracture in older people --- p.58Chapter 4 --- Bayesian diagnostics of two-level SEMs --- p.73Chapter 4.1 --- Model description --- p.74Chapter 4.2 --- Bayesian estimation and local inference of two-level SEMs --- p.75Chapter 4.3 --- Simulation study --- p.88Chapter 4.4 --- Application: A study of AIDS data --- p.91Chapter 5 --- Bayesian diagnostics of mixture SEMs --- p.106Chapter 5.1 --- Model description --- p.107Chapter 5.2 --- Bayesian estimation and local inference ofmixture SEMs --- p.108Chapter 5.3 --- Simulation study --- p.116Chapter 5.3.1 --- Simulation study 1 --- p.116Chapter 5.3.2 --- Simulation study 2 --- p.118Chapter 6 --- Conclusion --- p.126Bibliography --- p.130Chapter A --- Proof of Theorem 1.1 and 1.2 --- p.136Chapter B --- Full conditional distributions of the nonlinear SEM --- p.138Chapter C --- Full conditional distributions of the transformation SEM --- p.141Chapter D --- Full conditional distributions of the two-level SEM --- p.144Chapter E --- AIDS preventative intervention data --- p.150Chapter F --- Permutation sampler in the mixture SEM --- p.152Chapter G --- Full conditional distributions of the mixture SEM --- p.15