【作者】 刘育明；

【导师】 钱积新； 梁军；

【作者基本信息】 浙江大学 ， 控制科学与工程， 2006， 博士

【摘要】 本文研究的统计监控方法属于过程系统工程中的多变量统计过程控制(Multivariate Statistical Process Control,MSPC)领域,是一类既可离线分析又可在线实施的数据驱动(Data-driven)的方法。由于过程复杂的内在机制、各种随机噪声和干扰的存在、闭环控制器的广泛采用以及实时监控的要求,过程数据往往都存在着与时间相关的动态性,但是对于复杂的多变量系统要建立准确的动态模型十分困难,对于这种动态过程数据的多变量统计监控的是个非常有挑战性的问题。多变量统计监控从目的上来说,与常规的系统辨识、滤波或控制方法需要基于比较准确的动态系统模型来进行推断不同,只是侧重于对反映系统变动的统计指标的描述,籍此本文主要研究了与动态过程数据监控相关的非模型方法,这类方法从统计和统计学习的角度,结合了多元统计分析、质量控制、动态性质的描述和时频变换来完成对动态过程数据的多变量统计监控,避免复杂的模型参数的确定,本文的主要研究工作包括: (1) 针对常规MSPC方法在故障检测、故障变量辨识和故障识别中的难点,提出了多元特征提取方法与基于支持向量机(Support Vector Machine,SVM)的一类分类器设计、特征选择以及多类分类器设计方法相结合的一种完整的改进MSPC方法,其中故障检测方法去除了特征满足特定分布的假设前提,并可确定多个统计量的控制限;故障变量辨识方法中综合考虑了故障对于各个变量大小的影响以及变量变化对于故障分类的重要性,提高了关键变量选择的准确性;而故障识别方法是基于SVM对故障特征分类的优良特性,避免了传统判别法中经验准则的引入。上述方法在标准仿真问题Tennessee Eastman过程上结合主元分析(Principal Component Analysis,PCA)方法进行了应用,结果显示了其有效性; (2) 探讨了动态多变量数据在样本不独立且不满足正态分布的情况下的控制图修正方法,首先指出了用非参数方法调整控制限或者构建考虑了自相关特性的统计量的常规方法,在此基础上结合具有记忆效应的指数加权移动平均(Exponentially Weighted Moving Average,EWMA)控制图和滑动块自举(Moving Blocks Bootstrap,MBB)控制图处理自相关数据的优点,提出了一种改进的滑动块自举法eMBB(EWMA-Moving Blocks Bootstrap,eMBB),该方法首先采用特征提取算法获得隐变量,然后构造新的eMBB自举统计量,以适应更更多还原

【Abstract】 The statistical monitoring methods studied in this dissertation are substantially data-driven and may be applied to process monitoring either on-line or off-line. Generally speaking, all of these methods may be confined to the field of MSPC(multivariate statistical process control) in process system engineering. The common process data are dynamic in nature, which can be caused by the complicated process mechanism , random errors or disturbances, the extensive adoption of feed-back control or the requirement of real-time sampling, et al. However, it is difficult to build an dynamic model for a large multivariate system , which result in the monitoring of the dynamic process data a very challenge problem. Fortunately, compared to conventional system identification, filtering or control , which usually are based on an relatively accurate dynamic model, process monitoring has few requirements of the model, but focuses on how to describe the data variation, which may be reflected by some statistic indices. Therefore, in this thesis we mainly study the model-free methods related to dynamic process data monitoring. Combined with the knowledge in multivariate statistical analysis, statistical quality control, dynamic description and time-frequency transform, and based on statistical and statistical learning theory, the aforesaid model free methods can be applied to monitor dynamic process data effectively without much model parameters to determine.The main contents of this thesis are as follows: .(1) Fault detection, fault variable identification and fault identification are challenging problems in MSPC. A integrated novel MSPC method is proposed by combining multivariate feature extraction with three SVM-based methods commonly used in one-class classifier design, key feature selection and multi-class classifier design, respectively. The fiirst aspect of this method is its ablity to calculate control limits of multiple statistics for fault detection simultaneously without conventional theoretical distribution assumptions. Secondly, the method determine the key variables for fault identification based on both their magnitude changes and their contributions to fault classification in residual space, improving the identification accuracy. In the third method; Thirdly, fault identification is implemented by taking advantage of the well-known properties of SVM-based multi-class classifier which avoids introducing specific discriminant criteria. Using principal component analysis (PCA) as feature extraction method, the foresaid SVM-based MSPC method is illustrated with application to a benchmark simulator Tennessee Eastman process and its effectiveness is verified.(2) The methods to modify control charts for dynamic multivariate data which don’t following IID(Independent and Identical Distribution) assumption are studied. Firstly, the conventional methods either by adjusting control limits using non-parameter methods or by creating new statistic are introduced, then combining the advantages of EWMA(Exponentially Weighted Moving Average) control chart and MBB(Moving Block Bootstrap) control chart for monitoring auto-correlated data, an modified MBB method-eMBB(EWMA-MBB) is proposed.In this method, the latent variables are firstly extracted, then a new eMBB bootstrap statistic is defined to account for more extensive dynamics than conventional MBB. In a simulation example, for weakly dependent multivariate data, particularly in the case of small sample size, the foresaid eMBB and MBB method has advantage over the conventional PCA method according to their empirical ARL(Average Run Length) performance, and the empirical ARL of eMBB is closer to the theoretical value than that of MBB.(3) To overcome the difficulty to model the complex dynamic system, the conventional dynamic latent variable method is deeply explored to improve the statistical monitoring performance. Firstly, the properties of dynamic latent variable are confirmed to contain more dynamic information than conventional latent variable, but they own some of autocorrelation and cross-correlation. Thus, we suggest adopting the non-parametric methods to modify the control charts. For monitoring the residual space, the corresponding non-parameter methods are also recommended. In the second aspect, a method to choose the lagged variables and the time-lagged length is proposed, which taking process knowledge and empirical in-control ARL validation into account. The third feature in our research is proposing a new strategy to identify fault variable, which is based on cumulative sum of each variable’s residual and an RFE(Recursive Feature Elimination) algorithm. The properties of the foresaid methods are verified through two typical simulations.(4) In the face of autocorrelation and multiscale of process data, the application of time-frequency transform to monitor multivariate dynamic process is investigated based on the basic framework of MSPCA(Multiscale Principal Component Analysis). Firstly, the superiorities brought out by discrete wavelet transform are pointed out, as well as the properties of the scale features extracted by MSPCA, and the reason why MSPCA can substitute the conventional spectral PCA is illustrated. Secondly, two fault detection methods aiming at detecting abrupt fault and the fault with stationary scale features respectively ,are presented, therefore reinforce the basic MSPCA; thirdly, to identify the fault with stationary scale features , a SVM-based classifier is proposed using PCA to extract corresponding scale feature. The properties and effectiveness of the above methods are illustrated by application to an standard simulation process of CSTR(Continuous Stirred Tank Reactor).(5) Integrate all kinds of methods explored in the thesis and merge them into an statistical monitoring system which includes other related methods, and investigate the key points that will be met in industrial application. Lastly, the method to design the monitoring system of an industrial fluidized reactor is presented, and an statistical model for monitoring the chunk in the reactor is constructed based on the real process data.更多还原