基于数据驱动的复杂工业过程故障检测方法研究
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摘要
近年来,随着现代工业过程生产规模的不断扩大,企业生产系统的自动化水平和集成化程度也在不断提高。一方面生产单元之间的高关联性使得过程故障具有了比以往更强的破坏性。为了减少故障造成的诸如产品质量下降、生产成本提高等的影响,企业迫切需要运用有效的过程监控技术提高过程的安全性、增加系统的可靠性。而过程的复杂性给传统的过程监控方法带来了诸如建模困难等的问题。另一方面,现场总线技术和集散控制系统在工业过程中的广泛应用使得大量的工业过程数据通过成熟的数据采集与存储系统保存下来。在这样的背景下,利用过程数据建立监控模型的一类基于数据驱动的监控方法受到了广泛关注,并成为了过程监控领域中的一个研究热点。
目前,现有的基于数据驱动的故障检测方法在展开研究工作的过程中大多对过程数据设置多种简化或理想化的假设条件,比如数据的线性假设、高斯分布假设以及过程的单一运行模态假设等。但是,这些假设在一定程度上限制了监控方法在实际应用效果,本文拟针对流程工业过程监控中遇到的多种实际问题,在对传统多元统计监控(Multivariate Statistical Process Monitoring, MSPM)方法进行改进的同时,系统化地研究建立工业过程监控的新策略和新方法,以实现对过程数据具有高适应性的监控。本文的主要研究工作包括：
(1)针对非线性过程监控问题,提出一种统计量核主元分析方法(Statistics Kernel Principal Component Analysis, SKPCA)。通过计算样本集不同阶次的统计量将数据从原始数据空间映射到统计量样本空间,然后利用核函数将数据从统计量样本空间投影到高维核空间中进行核主元分析(Kernel Principal Component Analysis, KPCA)。由于统计量空间中包含了样本数据的高阶统计量,与传统KPCA相比SKPCA方法能够更有效的提取过程信息,也具有更好的监控效果。
(2)针对工业过程中存在运行模态发生切换的情况,提出一种基于距离空间统计量分析(Distance Space Statistics Analysis, DSSA)的多模态过程故障检测方法。在对多模态数据空间分布特性进行分析的基础上,对正常数据与故障数据彼此之间的距离关系进行比较,构建样本在距离空间中的表达形式。运用统计量分析方法提取高阶统计信息,实现了对多模态过程只建立单一监控模型的故障检测。
(3)针对工业过程中的多模态问题,从对数据进行预处理的角度出发,提出一种新的局部邻域标准化策略(Local Neighborhood Standardization, LNS),(?)局部邻域标准化策略与主元分析方法(Principal Component Analysis, PCA)结合,得到一种新的LNS-PCA方法对多模态过程进行故障检测。利用这种数据预处策略对多模态数据进行标准化能够有效的抹除数据中包含的多分布特征,同时不会破坏变量之间的相关关系,监控结果能够通过变量的贡献度分解进行解释,从而实现对故障变量的识别。LNS与PCA方法结合之后能够使PCA方法对过程数据中的多模态特征具有鲁棒性。
(4)针对工业过程中同一运行模态下的数据分布较为复杂,无法满足高斯分布的情况,提出一种基于密度的马氏距离局部离群因子方法(Mahalanobis Distance-based Local Outlier Factor, MDLOF)进行故障检测。通过在计算样本局部离群因子的过程中引入马氏距离对变量的局部结构以及样本的局部邻域密度加以考虑。利用基于密度的局部离群因子作为监控指标,形成对样本偏离正常状态程度的度量。与传统MSPM方法利用的基于距离的监控统计量不同,MDLOF能够利用局部邻域信息,同时对数据的分布情况没有依赖性。
(5)针对过程数据复杂分布的情况下模态内数据分布不确定,同时又包含多模态特征的问题,结合局部邻域标准化策略与局部离群因子方法,提出一种新的邻域标准化局部离群因子方法(Neighborhood Standardized Local Outlier Factor, NSLOF)。借鉴提出的局部邻域标准化策略对数据的处理方式,运用一种新的加权欧式距离计算样本的局部离群因子。加权欧式距离的应用使得NSLOF方法中的监控指标能够直接进行变量贡献度分解,从而实现了基于局部离群因子方法进行过程监控时的故障变量识别问题。同时,降低了局部离群因子方法在实际应用过程中的计算复杂度,提高了监控高性能。
在对上述方法进行分析的同时,通过不同方法之间的比较,利用数值仿真例子以及Tennessee Eastman过程进行验证,仿真结果验证了本文方法的有效性。最后,在对本文主要工作进行总结的基础上对未来的研究工作进行了展望。
In the past few years, the scales of the modern industrial processes are increased continuously. The production systems have become more and more integrated and complex with the developments of automatic control techniques. On the one hand, the interactions among different manufacturing plants make the faults more destructive for the process. To reduce the impacts of the process faults such as the decline of the product quality and the increase of the product costs, the effective process monitoring becomes one of the most urgent problems in modern industrial processes. It is important to improve the security of the industrial processes and increase the system reliability. However, the complexity of the processes makes the monitoring models difficult to be built by the traditional methods. On the other hand, with the filedbus tenology and the distributed control systems widely used, a large number of the historical operating data can be measured and stored automatically by the data acquisition and storage system system. In this context, data-driven monitoring methods have been intensively researched and arisen as an active research area in process monitoring.
However, most of the traditional data-based fault detection methods often contain some assumptions such as linear, Gaussian and unimodal processes. All of assumptions limit the monitoring performance of the monitoring methods when applied in practical processes.To deal with the practical monitoring challenges, some improvements of the conventional multivariate statistical process monitoring methods and several novel monitoring strategies are propsed in this dissertation, which are summarized as follows:
(1) For nonlinear process monitoring, a fault detection method called statistics kernel principal component analysis (SKPCA) is developed. The original data space is projected into a statistics space by the calculation of the the higher-order statistics of the data set. Then, KPCA is conducted in the statistics space to extract some dominant principal components. Since the higher-order statistics are involved in the statistics space, SKPCA can extract more meaningful knowledge and obtain a better monitoring results than KPCA
(2) For multimode process monitoting, a novel multimode fault detection approach named distance space statistics analysis (DSSA) is proposed. Based on the analysis of the distribution characteristics of the multimode data, the distance relationships of the normal samples and the fault samples are compared. Then, the expressions of the samples in the original data space are obtained to build the distance space. Finally, the statistics analysis method is employed to extract the useful informations in the higher-order statistics. The proposed DSSA method use only one single model for multimode process monitoring.
(3) In order to improve the monitoring performance for multimode process, a novel local neighborhood standardization (LNS) strategy is proposed as a data preprocessing method to address the challenges caused by the multimode characteristic of operating data. After a thorough analysis of LNS, the proposed data preprocessing method is integrated with principal component analysis method to provide a complete fault detection method in multimode processes. By using LNS, the multimode characteristic of operating data can be erased in the data processing phase while the correlations of variables are maintained. The contribution-based fault identification method can be directly applied in the LNS-PCA method. The new data preprocessing method makes the PCA method display a robust performance regardless of the multimode characteristic in process data.
(4) The process complexity makes the within-mode data usually follow an uncertain combination of Gaussian and non-Gaussian distribution. A novel Mahalanobis distance-based local outlier factor (MDLOF) method is proposed to deal with the complex distribution of the process data. The local structure of variables is taken into account by employing Mahalanobis distances, and the local density of the surrounding neighborhoods is also considered. The density-based local outlier factor is employed as a monitoring statistic. The degree of the oulierness is calculated for each sample. Different from the traditional distance-based monitoring methods, the information in the local neighborhood is utilized in MDLOF. The density-based monitoring index makes MDLOF effective regardless of the data distribution.
(5) To develop an efficient monitoring method with the multimodality and the within-mode uncertainty of data distribution, a novel neighborhood standardized local outlier factor (NSLOF) method is proposed by integrating the LNS and local outlier factor. A new normalized Euclidean distance based on the local neighborhood standardization strategy is employed during the calculation of the monitoring index. The utilization of the normalized Euclidean distance makes the contribution-based fault identification method available for the NSLOF method. Meanwhile, compared to the MDLOF, the monitoring performance is improved and the algorithm complexity is reduced.
Several monitoring methods are compared with the proposed methods, and the numerical example and the Tennessee Eastman process are applied to illustrate the efficient of the proposed methods. Finally, based on the conclusion of the thesis, some future research directions are discussed.
目前,现有的基于数据驱动的故障检测方法在展开研究工作的过程中大多对过程数据设置多种简化或理想化的假设条件,比如数据的线性假设、高斯分布假设以及过程的单一运行模态假设等。但是,这些假设在一定程度上限制了监控方法在实际应用效果,本文拟针对流程工业过程监控中遇到的多种实际问题,在对传统多元统计监控(Multivariate Statistical Process Monitoring, MSPM)方法进行改进的同时,系统化地研究建立工业过程监控的新策略和新方法,以实现对过程数据具有高适应性的监控。本文的主要研究工作包括：
(1)针对非线性过程监控问题,提出一种统计量核主元分析方法(Statistics Kernel Principal Component Analysis, SKPCA)。通过计算样本集不同阶次的统计量将数据从原始数据空间映射到统计量样本空间,然后利用核函数将数据从统计量样本空间投影到高维核空间中进行核主元分析(Kernel Principal Component Analysis, KPCA)。由于统计量空间中包含了样本数据的高阶统计量,与传统KPCA相比SKPCA方法能够更有效的提取过程信息,也具有更好的监控效果。
(2)针对工业过程中存在运行模态发生切换的情况,提出一种基于距离空间统计量分析(Distance Space Statistics Analysis, DSSA)的多模态过程故障检测方法。在对多模态数据空间分布特性进行分析的基础上,对正常数据与故障数据彼此之间的距离关系进行比较,构建样本在距离空间中的表达形式。运用统计量分析方法提取高阶统计信息,实现了对多模态过程只建立单一监控模型的故障检测。
(3)针对工业过程中的多模态问题,从对数据进行预处理的角度出发,提出一种新的局部邻域标准化策略(Local Neighborhood Standardization, LNS),(?)局部邻域标准化策略与主元分析方法(Principal Component Analysis, PCA)结合,得到一种新的LNS-PCA方法对多模态过程进行故障检测。利用这种数据预处策略对多模态数据进行标准化能够有效的抹除数据中包含的多分布特征,同时不会破坏变量之间的相关关系,监控结果能够通过变量的贡献度分解进行解释,从而实现对故障变量的识别。LNS与PCA方法结合之后能够使PCA方法对过程数据中的多模态特征具有鲁棒性。
(4)针对工业过程中同一运行模态下的数据分布较为复杂,无法满足高斯分布的情况,提出一种基于密度的马氏距离局部离群因子方法(Mahalanobis Distance-based Local Outlier Factor, MDLOF)进行故障检测。通过在计算样本局部离群因子的过程中引入马氏距离对变量的局部结构以及样本的局部邻域密度加以考虑。利用基于密度的局部离群因子作为监控指标,形成对样本偏离正常状态程度的度量。与传统MSPM方法利用的基于距离的监控统计量不同,MDLOF能够利用局部邻域信息,同时对数据的分布情况没有依赖性。
(5)针对过程数据复杂分布的情况下模态内数据分布不确定,同时又包含多模态特征的问题,结合局部邻域标准化策略与局部离群因子方法,提出一种新的邻域标准化局部离群因子方法(Neighborhood Standardized Local Outlier Factor, NSLOF)。借鉴提出的局部邻域标准化策略对数据的处理方式,运用一种新的加权欧式距离计算样本的局部离群因子。加权欧式距离的应用使得NSLOF方法中的监控指标能够直接进行变量贡献度分解,从而实现了基于局部离群因子方法进行过程监控时的故障变量识别问题。同时,降低了局部离群因子方法在实际应用过程中的计算复杂度,提高了监控高性能。
在对上述方法进行分析的同时,通过不同方法之间的比较,利用数值仿真例子以及Tennessee Eastman过程进行验证,仿真结果验证了本文方法的有效性。最后,在对本文主要工作进行总结的基础上对未来的研究工作进行了展望。
In the past few years, the scales of the modern industrial processes are increased continuously. The production systems have become more and more integrated and complex with the developments of automatic control techniques. On the one hand, the interactions among different manufacturing plants make the faults more destructive for the process. To reduce the impacts of the process faults such as the decline of the product quality and the increase of the product costs, the effective process monitoring becomes one of the most urgent problems in modern industrial processes. It is important to improve the security of the industrial processes and increase the system reliability. However, the complexity of the processes makes the monitoring models difficult to be built by the traditional methods. On the other hand, with the filedbus tenology and the distributed control systems widely used, a large number of the historical operating data can be measured and stored automatically by the data acquisition and storage system system. In this context, data-driven monitoring methods have been intensively researched and arisen as an active research area in process monitoring.
However, most of the traditional data-based fault detection methods often contain some assumptions such as linear, Gaussian and unimodal processes. All of assumptions limit the monitoring performance of the monitoring methods when applied in practical processes.To deal with the practical monitoring challenges, some improvements of the conventional multivariate statistical process monitoring methods and several novel monitoring strategies are propsed in this dissertation, which are summarized as follows:
(1) For nonlinear process monitoring, a fault detection method called statistics kernel principal component analysis (SKPCA) is developed. The original data space is projected into a statistics space by the calculation of the the higher-order statistics of the data set. Then, KPCA is conducted in the statistics space to extract some dominant principal components. Since the higher-order statistics are involved in the statistics space, SKPCA can extract more meaningful knowledge and obtain a better monitoring results than KPCA
(2) For multimode process monitoting, a novel multimode fault detection approach named distance space statistics analysis (DSSA) is proposed. Based on the analysis of the distribution characteristics of the multimode data, the distance relationships of the normal samples and the fault samples are compared. Then, the expressions of the samples in the original data space are obtained to build the distance space. Finally, the statistics analysis method is employed to extract the useful informations in the higher-order statistics. The proposed DSSA method use only one single model for multimode process monitoring.
(3) In order to improve the monitoring performance for multimode process, a novel local neighborhood standardization (LNS) strategy is proposed as a data preprocessing method to address the challenges caused by the multimode characteristic of operating data. After a thorough analysis of LNS, the proposed data preprocessing method is integrated with principal component analysis method to provide a complete fault detection method in multimode processes. By using LNS, the multimode characteristic of operating data can be erased in the data processing phase while the correlations of variables are maintained. The contribution-based fault identification method can be directly applied in the LNS-PCA method. The new data preprocessing method makes the PCA method display a robust performance regardless of the multimode characteristic in process data.
(4) The process complexity makes the within-mode data usually follow an uncertain combination of Gaussian and non-Gaussian distribution. A novel Mahalanobis distance-based local outlier factor (MDLOF) method is proposed to deal with the complex distribution of the process data. The local structure of variables is taken into account by employing Mahalanobis distances, and the local density of the surrounding neighborhoods is also considered. The density-based local outlier factor is employed as a monitoring statistic. The degree of the oulierness is calculated for each sample. Different from the traditional distance-based monitoring methods, the information in the local neighborhood is utilized in MDLOF. The density-based monitoring index makes MDLOF effective regardless of the data distribution.
(5) To develop an efficient monitoring method with the multimodality and the within-mode uncertainty of data distribution, a novel neighborhood standardized local outlier factor (NSLOF) method is proposed by integrating the LNS and local outlier factor. A new normalized Euclidean distance based on the local neighborhood standardization strategy is employed during the calculation of the monitoring index. The utilization of the normalized Euclidean distance makes the contribution-based fault identification method available for the NSLOF method. Meanwhile, compared to the MDLOF, the monitoring performance is improved and the algorithm complexity is reduced.
Several monitoring methods are compared with the proposed methods, and the numerical example and the Tennessee Eastman process are applied to illustrate the efficient of the proposed methods. Finally, based on the conclusion of the thesis, some future research directions are discussed.
引文
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