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

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Xiamen University Institutional Repository

学校编码：10384 学号：23220131153375         硕  士  学  位  论  文         贝叶斯故障诊断方法的改进及其应用研究         Research on Improvement and Applicationsfor Bayesian Fault Diagnosis         朱文炳         指导教师：周笋             专业名称：工程硕士(控制工程)         答辩日期：2016年5月   厦门大学博硕士论文摘要库厦门大学学位论文原创性声明                  本人呈交的学位论文是本人在导师指导下,独立完成的研究成果。本人在论文写作中参考其他个人或集体已经发表的研究成果，均在文中以适当方式明确标明，并符合法律规范和《厦门大学研究生学术活动规范(试行)》。                  另外，该学位论文为(                  )课题(组)的研究成果，获得(                    )课题(组)经费或实验室的资助，在(          )实验室完成。(请在以上括号内填写课题或课题组负责人或实验室名称，未有此项声明内容的，可以不作特别声明。)                           声明人(签名)：                            年   月   日厦门大学博硕士论文摘要库厦门大学学位论文著作权使用声明                  本人同意厦门大学根据《中华人民共和国学位条例暂行实施办法》等规定保留和使用此学位论文，并向主管部门或其指定机构送交学位论文(包括纸质版和电子版)，允许学位论文进入厦门大学图书馆及其数据库被查阅、借阅。本人同意厦门大学将学位论文加入全国博士、硕士学位论文共建单位数据库进行检索，将学位论文的标题和摘要汇编出版，采用影印、缩印或者其它方式合理复制学位论文。                  本学位论文属于：                  (     )1.经厦门大学保密委员会审查核定的保密学位论文，于年 月 日解密，解密后适用上述授权。                  (     )2.不保密，适用上述授权。                  (请在以上相应括号内打“√”或填上相应内容。保密学位论文应是已经厦门大学保密委员会审定过的学位论文，未经厦门大学保密委员会审定的学位论文均为公开学位论文。此声明栏不填写的，默认为公开学位论文，均适用上述授权。)                           声明人(签名)：                            年   月   日厦门大学博硕士论文摘要库摘  要                         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，系统介绍了控制回路贝叶斯诊断的基本原理。(2)针对贝叶斯诊断中证据的“维数灾难”问题，研究了三种典型的降维方法，它们分别是无监督降维、有监督降维、基于独立性降维的典型代表。(3)针对贝叶斯诊断中似然估计的精度问题，研究了三种典型的似然估计方法，它们分别是离散似然估计方法、连续似然估计方法的典型代表，以及为了更好地结合先验知识，所提出的平均移动似然估计方法。(4)针对证据离散化造成信息损失的问题、“维数灾难”问题、贝叶斯诊断无法有效结合先验知识的问题，分别提出了三种特定条件下的贝叶斯诊断解决方案：基于优化直方图估计和主成分分析的诊断、基于线性判别分析和核密度估计的诊断、基于平均移动似然估计结合先验知识的诊断。         关键词：故障检测与诊断；数据驱动；贝叶斯诊断；特征提取；似然估计厦门大学博硕士论文摘要库Abstract                        The purpose of control loop detection and diagnosis is to ensure the safetyand efficacy of the production process, reduce maintenance costs and downtime.Bayesian diagnosis is a probabilistic diagnosis framework of control loopmonitoring, which can combine multiple monitor technology to build a diagnosissystem and make an optimal decision. However, there are many differentsituations in the control loop diagnosis for industrial process. The performance ofBayesian diagnosis is severely restricted. In this paper, we focus on the improvedmethod of Bayesian diagnosis to enhance the diagnostic performance throughdimension reduction and likelihood estimation, which proposed the evidencediscretization method based on histogram estimation optimization, the featureextraction and dimension reduction based on linear discriminant analysis and theaveraged shifted likelihood estimation method. Simulated system, bench scalesystem and industrial scale system verify the effectiveness of the proposedmethod. The major work of this thesis includes:(1)A review of the existing methods and the research status for Bayesiandiagnosis is made, the basic principle of Bayesian diagnosis is introduced in thispaper.(2)Accounting for the problem of "Curse of dimensionality" in Bayesian diagnosis,three typical dimension reduction methods are studied, which are the typicalrepresentative of unsupervised dimensionality reduction, superviseddimensionality reduction and independence based dimensionality reduction.(3)Accounting for the problem of likelihood estimation accuracy for Bayesiandiagnosis, three kinds of typical likelihood estimation method are studied, whichare typical representative of discrete likelihood estimation method and continuouslikelihood estimation method. In order to better integrate the prior knowledge, weproposed averaged shift likelihood estimation method.厦门大学博硕士论文摘要库(4)Accounting for information loss of evidence discretization, "Curse ofDimensionality" problem, Bayesian diagnosis can not be effectively combined withprior knowledge. Three specific solutions of Bayesian diagnosis are put forward:diagnosis base on principal component analysis and optimal histogram, diagnosisbase on linear discriminant analysis and kernel density estimation, diagnosis baseon averaged shifted likelihood estimation and prior knowledge.         Keywords: Fault detection and diagnosis; Data-driven; Bayesian diagnosis;Feature extraction; Likelihood estimation厦门大学博硕士论文摘要库参考资料         [1]Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri SN. A review of process fault detection and diagnosis:Part I: Quantitative model-based methods [J]. Computers & chemical engineering, 2003, 27(3); 293–311.[2]Venkatasubramanian V, Rengaswamy R, Kavuri SN. 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If your library is a CALIS member libraries, please log on http://etd.calis.edu.cn/ and submit requests online, or consult the interlibrary loan department in your library. 2. For users of non-CALIS member libraries, please mail to etd@xmu.edu.cn for delivery details. 厦门大学博硕士论文摘要库

Research on Improvement and Applications for Bayesian Fault Diagnosis

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Research on Improvement and Applications for Bayesian Fault Diagnosis

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