融合聚类分析的故障检测和分类研究
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摘要
过程安全、产品质量以及环境保护是现代流程工业的核心目标,因此过程监测作为过程自动化系统的重要组成部分和关键技术之一,具有重大的现实意义和价值。随着集散控制系统在工业过程中的广泛应用以及计算机存储技术的飞速发展,海量过程数据得以收集和存储。由于缺少充足的过程经验和应用工具,工业过程往往数据丰富但知识缺乏。因此,基于多元统计分析和模式识别等数据驱动过程监测成为研究热点,在这十多年来产生了许多研究成果和工业应用。
但是,传统的多元统计分析和模式识别方法没有考虑实际工业过程中存在的样本类标签未知、各故障数据不平衡、非线性、多工况、瞬态等诸多问题。本文在已有方法基础上,通过融入聚类分析思想,分析数据预处理、特征提取和模式分类等诸多环节,针对不同问题,提出了一套融合聚类分析的故障检测和分类新方法和新框架：
(1)针对工业过程所提取的训练样本不纯时核主元分析(KPCA)失效问题,提出了一种有效融合Fisher判别式分析-可能性c-均值聚类(FDA-PCMC)的KPCA新方法。首先应用FDA来提取特征并实现训练样本的初分类,然后应用PCMC来有效聚类训练样本。即首先通过分类聚类混杂学习实现训练样本纯化,然后使用KPCA实现非线性故障检测。
(2)工业过程中存在正常样本多,故障样本少,而且不同故障类别的样本量各不相同的问题。分类器倾向于大类数据,而过程监测的小类数据是关注焦点,由此产生了当数据集存在不平衡问题时核Fisher判别式分析(KFDA)性能不佳的问题。本文提出了一种基于非平衡校正的KFDA故障分类新方法--诱导偏移KFDA,该方法通过在基于最小欧氏距离的模式判别准则上融入一个新的权重调整矩阵,实现了非平衡校正的目的。
(3)多步和单步模式下的过程监测研究。首先具体阐述了基于模式识别的故障检测和分类系统下的多步和单步模式,然后在多步模式下,提出了基于主元分析(PCA)-支持向量数据描述(SVDD)的故障检测和分类新方法。在此基础上,分析和讨论了PCA和KPCA的相互关联和性能差异,提出了在多步和单步模式下的基于KPCA-SVDD故障检测和分类方法。最后为实现最优模式选取,构建了一个新的基于过程监测系统全局损失的评价准则。评价准则除了考虑分类器的故障检测和故障分类性能外,还考虑了故障检测和故障分类的误分类代价。
(4)基于特征判别子空间的故障分类研究。基于FDA和KFDA的特征提取,可将原始数据空间投影到特征判别线性和非线性子空间上。本文首先讨论了监督学习在特征提取上的重要性。然后提出了FDA特征提取、Fisher线性分类和SVDD非线性分类相结合的算法,给出了串级和混联两个融合方式,实现了故障模式的有效分类。最后在KFDA特征子空间上提出了基于混合高斯模型(GMM)和к最近邻(kNN)分类器的故障分类方法,并讨论了参数和非参数分类器的性能差异。
(5)为实现过程多工况的辨识和故障检测,首先提出了一种融合移动窗技术的集成聚类新方法,该方法在基于独立元分析-主元分析(ICA-PCA)方法两步提取特征后,基于k-ICA-PCA模型的两层集成聚类可实现多工况建模。在此基础上,建立了一个基于多独立元分析-主元分析邻接模型,实现了多工况过程的辨识和故障检测。
(6)针对多工况间的瞬态过程监测问题,提出了一种融合动态集成聚类的瞬态过程监测方法。为了获取瞬态过程数据的动态、非高斯特性并类标签化瞬态过程,提出了一种面向瞬态过程模式分析的集成聚类方法,建立了一个新型的动态k-ICA-PCA模型。然后使用基于主元分析的特征提取和基于多类SVDD的模式分类来实现瞬态过程的监测。
最后,总结了本文的主要研究成果,并阐述了未来的研究工作。
Process safety, product quality and environmental protection are the core objectives of the modern industrial processes. As the important part and one of the key technologies in process automation systems, process monitoring has an important and realistic role in meeting these objectives. With the wide application of the distributed control systems in the industrial processes and the rapid development of computer storage technologies, a large number of process data can be well collected and stored. While rich in data, process knowledge is often lacking due to insufficient expertise and lack of practice tools. Multivariate statistical analysis and pattern recognition based data-driven process monitoring has become a research hotspot, and many research results and industrial applications have been obtained in the past decade, to address this challenge.
Traditional multivariate statistical analysis and pattern recognition methods do not fully address the complex problems in the industrial process, such as labeling training samples, data imbalance of different faults, nonlinear, multiple operating modes, transition process, and so on. After studying existing methods, integrating clustering analysis and analyzing data preprocessing, feature extraction and pattern classification methods, by integrating clustering analysis, several efficient and effective fault detection and classification methods are proposed to address these problems.
(1) To solve the problem of fault detection when training samples of the industrial process is impure, a new kernel principal component analysis (KPCA) algorithm integrating Fisher discriminant analysis-possibilistic c-means clustering-(FDA-PCMC) is presented. FDA is first applied to extract feature and pre-classify the training samples, and then PCMC is applied to cluster the training samples effectively. This means that a hybrid learning including classification and clustering is firstly presented to purify the training samples, and then, KPCA is applied for nonlinear fault detection.
(2) Normal process is the majority pattern in an industrial process, while faulty patterns are the minority ones. Moreover, there will be different number of samples periods for each fault. Classifiers generally pay more attention to the majority pattern, while the learning concern should be the minority ones, which makes kernel Fisher discriminant analysis (KFDA) perform poorly when the data sets have an imbalance problem. In the thesis, a novel imbalance modified kernel Fisher discriminant analysis (IM-KFDA) approach named inductive bias KFDA is proposed. To handle the imbalance problem, a novel regular weighted matrix is incorporated into the minimum Euclid distance based pattern classification rule.
(3) Research on simultaneous form and serial form based process monitoring. First of all, serial form and simultaneous form of pattern recognition based fault detection and classification are discussed. And then, under the serial form, a novel fault detection and classification method based on principal component analysis-support vector data description (PCA-SVDD) is proposed. Moreover, after discussing the relationship and difference between PCA and KPCA, under serial form and simultaneous form, two novel fault detection and classification approaches integrating KPCA and SVDD are proposed subsequently. At last, a performance assessment rule based on the monitoring system overall loss is presented. Beside of considering the fault detection and classification performance of the classifiers, the mis-classification costs in both of the fault detection and classification stages have also been considered.
(4)Research on fault classification based on feature discrimination subspace. FDA and KFDA based feature extraction can project the original data space into the feature discrimination linear and nonlinear subspace, respectively. In this thesis, first of all, the importance of supervised learning for feature extraction is discussed. And then, FDA based feature extraction, Fisher linear classification and SVDD based nonlinear classification are proposed for faulty mode isolation. The cascade and series parallel combination forms are also proposed when incorporating FDA and SVDD. Finally, Gaussian mixture model (GMM) and k-nearest neighbor (kNN) classifiers based fault classification on KFDA feature subspace are proposed, and performance discussions of parameter and non-parameter classifiers are subsequently presented.
(5) To accomplish multimode process identification and fault detection, a new bilayer ensemble clustering approach incorporating moving window is proposed. After two-step independent component analysis-principal component analysis (ICA-PCA) based feature extraction, k-ICA-PCA models based bilayer ensemble clustering are proposed for multimode process mode construction, and then, adjoined multiple ICA-PCA models are presented for multimode process identification and fault detection.
(6) To solve the fault detection problem of transition process, a dynamic ensemble clustering integrated transition process monitoring approach is presented. To obtain the dynamic and non-Gaussian feature of the transition process data and to label the transition process, an ensemble clustering approach is proposed for transition process analysis, and a new dynamic k-ICA-PCA model is also accomplished. And then, PCA based feature extraction and SVDD based pattern classification are used for transition process monitoring.
Finally, research results in this thesis are concluded, and then the future work is discussed.
但是,传统的多元统计分析和模式识别方法没有考虑实际工业过程中存在的样本类标签未知、各故障数据不平衡、非线性、多工况、瞬态等诸多问题。本文在已有方法基础上,通过融入聚类分析思想,分析数据预处理、特征提取和模式分类等诸多环节,针对不同问题,提出了一套融合聚类分析的故障检测和分类新方法和新框架：
(1)针对工业过程所提取的训练样本不纯时核主元分析(KPCA)失效问题,提出了一种有效融合Fisher判别式分析-可能性c-均值聚类(FDA-PCMC)的KPCA新方法。首先应用FDA来提取特征并实现训练样本的初分类,然后应用PCMC来有效聚类训练样本。即首先通过分类聚类混杂学习实现训练样本纯化,然后使用KPCA实现非线性故障检测。
(2)工业过程中存在正常样本多,故障样本少,而且不同故障类别的样本量各不相同的问题。分类器倾向于大类数据,而过程监测的小类数据是关注焦点,由此产生了当数据集存在不平衡问题时核Fisher判别式分析(KFDA)性能不佳的问题。本文提出了一种基于非平衡校正的KFDA故障分类新方法--诱导偏移KFDA,该方法通过在基于最小欧氏距离的模式判别准则上融入一个新的权重调整矩阵,实现了非平衡校正的目的。
(3)多步和单步模式下的过程监测研究。首先具体阐述了基于模式识别的故障检测和分类系统下的多步和单步模式,然后在多步模式下,提出了基于主元分析(PCA)-支持向量数据描述(SVDD)的故障检测和分类新方法。在此基础上,分析和讨论了PCA和KPCA的相互关联和性能差异,提出了在多步和单步模式下的基于KPCA-SVDD故障检测和分类方法。最后为实现最优模式选取,构建了一个新的基于过程监测系统全局损失的评价准则。评价准则除了考虑分类器的故障检测和故障分类性能外,还考虑了故障检测和故障分类的误分类代价。
(4)基于特征判别子空间的故障分类研究。基于FDA和KFDA的特征提取,可将原始数据空间投影到特征判别线性和非线性子空间上。本文首先讨论了监督学习在特征提取上的重要性。然后提出了FDA特征提取、Fisher线性分类和SVDD非线性分类相结合的算法,给出了串级和混联两个融合方式,实现了故障模式的有效分类。最后在KFDA特征子空间上提出了基于混合高斯模型(GMM)和к最近邻(kNN)分类器的故障分类方法,并讨论了参数和非参数分类器的性能差异。
(5)为实现过程多工况的辨识和故障检测,首先提出了一种融合移动窗技术的集成聚类新方法,该方法在基于独立元分析-主元分析(ICA-PCA)方法两步提取特征后,基于k-ICA-PCA模型的两层集成聚类可实现多工况建模。在此基础上,建立了一个基于多独立元分析-主元分析邻接模型,实现了多工况过程的辨识和故障检测。
(6)针对多工况间的瞬态过程监测问题,提出了一种融合动态集成聚类的瞬态过程监测方法。为了获取瞬态过程数据的动态、非高斯特性并类标签化瞬态过程,提出了一种面向瞬态过程模式分析的集成聚类方法,建立了一个新型的动态k-ICA-PCA模型。然后使用基于主元分析的特征提取和基于多类SVDD的模式分类来实现瞬态过程的监测。
最后,总结了本文的主要研究成果,并阐述了未来的研究工作。
Process safety, product quality and environmental protection are the core objectives of the modern industrial processes. As the important part and one of the key technologies in process automation systems, process monitoring has an important and realistic role in meeting these objectives. With the wide application of the distributed control systems in the industrial processes and the rapid development of computer storage technologies, a large number of process data can be well collected and stored. While rich in data, process knowledge is often lacking due to insufficient expertise and lack of practice tools. Multivariate statistical analysis and pattern recognition based data-driven process monitoring has become a research hotspot, and many research results and industrial applications have been obtained in the past decade, to address this challenge.
Traditional multivariate statistical analysis and pattern recognition methods do not fully address the complex problems in the industrial process, such as labeling training samples, data imbalance of different faults, nonlinear, multiple operating modes, transition process, and so on. After studying existing methods, integrating clustering analysis and analyzing data preprocessing, feature extraction and pattern classification methods, by integrating clustering analysis, several efficient and effective fault detection and classification methods are proposed to address these problems.
(1) To solve the problem of fault detection when training samples of the industrial process is impure, a new kernel principal component analysis (KPCA) algorithm integrating Fisher discriminant analysis-possibilistic c-means clustering-(FDA-PCMC) is presented. FDA is first applied to extract feature and pre-classify the training samples, and then PCMC is applied to cluster the training samples effectively. This means that a hybrid learning including classification and clustering is firstly presented to purify the training samples, and then, KPCA is applied for nonlinear fault detection.
(2) Normal process is the majority pattern in an industrial process, while faulty patterns are the minority ones. Moreover, there will be different number of samples periods for each fault. Classifiers generally pay more attention to the majority pattern, while the learning concern should be the minority ones, which makes kernel Fisher discriminant analysis (KFDA) perform poorly when the data sets have an imbalance problem. In the thesis, a novel imbalance modified kernel Fisher discriminant analysis (IM-KFDA) approach named inductive bias KFDA is proposed. To handle the imbalance problem, a novel regular weighted matrix is incorporated into the minimum Euclid distance based pattern classification rule.
(3) Research on simultaneous form and serial form based process monitoring. First of all, serial form and simultaneous form of pattern recognition based fault detection and classification are discussed. And then, under the serial form, a novel fault detection and classification method based on principal component analysis-support vector data description (PCA-SVDD) is proposed. Moreover, after discussing the relationship and difference between PCA and KPCA, under serial form and simultaneous form, two novel fault detection and classification approaches integrating KPCA and SVDD are proposed subsequently. At last, a performance assessment rule based on the monitoring system overall loss is presented. Beside of considering the fault detection and classification performance of the classifiers, the mis-classification costs in both of the fault detection and classification stages have also been considered.
(4)Research on fault classification based on feature discrimination subspace. FDA and KFDA based feature extraction can project the original data space into the feature discrimination linear and nonlinear subspace, respectively. In this thesis, first of all, the importance of supervised learning for feature extraction is discussed. And then, FDA based feature extraction, Fisher linear classification and SVDD based nonlinear classification are proposed for faulty mode isolation. The cascade and series parallel combination forms are also proposed when incorporating FDA and SVDD. Finally, Gaussian mixture model (GMM) and k-nearest neighbor (kNN) classifiers based fault classification on KFDA feature subspace are proposed, and performance discussions of parameter and non-parameter classifiers are subsequently presented.
(5) To accomplish multimode process identification and fault detection, a new bilayer ensemble clustering approach incorporating moving window is proposed. After two-step independent component analysis-principal component analysis (ICA-PCA) based feature extraction, k-ICA-PCA models based bilayer ensemble clustering are proposed for multimode process mode construction, and then, adjoined multiple ICA-PCA models are presented for multimode process identification and fault detection.
(6) To solve the fault detection problem of transition process, a dynamic ensemble clustering integrated transition process monitoring approach is presented. To obtain the dynamic and non-Gaussian feature of the transition process data and to label the transition process, an ensemble clustering approach is proposed for transition process analysis, and a new dynamic k-ICA-PCA model is also accomplished. And then, PCA based feature extraction and SVDD based pattern classification are used for transition process monitoring.
Finally, research results in this thesis are concluded, and then the future work is discussed.
引文
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