【作者】 王闯；

【导师】 张颖伟；

【作者基本信息】 东北大学 ， 控制理论与控制工程， 2013， 硕士

【摘要】 随着现代工业过程规模的扩大及流程的复杂化,保证过程安全和提高产品质量是企业迫切需要解决的两个问题,过程监测技术是解决这两个问题的有效途径。由于集散控制系统(DCS)在工业中的广泛应用,使得过程的数据信息规模越来越大,多变量统计过程监控(MSPM)已被广泛的应用于解决故障检测的问题。传统的MSPM方法对工业过程的限制条件较多,如过程数据要服从高斯分布、线：性、稳定单一工况等。然而,在实际工业过程中,过程信息十分复杂,其服从何种分布很难确定；且生产过程往往不只有一个稳定工况,大部分生产面临的过程是多模式过程。本文在前人工作的基础上,针对多模式工业过程中数据的不同统计特性,做了以下的研究工作：(1)针对多模式生产过程,传统的多变量统计分析方法没有考虑模式之间的相关性,这里从多模式的全局分析出发,通过流形算法局部线性嵌入(Locally Linearly Embedding,LLE)提取非线性多模式数据间的公共的变化特性信息,称之为公共部分,之后得到各模式的特殊部分。公共部分表示的是模式之间具有相同变化规律的部分,特殊部分表示的是各个模式所特有的特性,并分别建立KPCA模型。通过对多模式公共部分的全局监测与各模式特殊部分的监测,更好地了解多模式过程和进行有效的故障检测。通过电熔镁炉工作过程实例的仿真证明了此方法的可行性。(2)实际工业生产过程数据往往具有很强的非线性和非高斯性,而主元分析对信号数据的处理过程只涉及到信号数据二阶特性,并未考虑到信号数据的高阶统计特性,所以变换后的数据间仍可能存在高阶冗余信息。并且,主元分析方法仅解除了数据间的相关性,并未对其独立性问题进行相应的分析,这使得基于主元分析及其改进算法的多元统计过程监控方法在上述工业过程中的应用不是很理想。基于KPCA的局限性,这里提出KLLE-KICA来处理多模式过程中数据的非线性、非高斯建模问题。将此方法应用于冷轧连续退火过程中进行过程监控,仿真结果表明,该方法能降低误报警,提高故障检测的准确性。更多还原

【Abstract】 With the expansion of the scale of modern industrial and the complication of the process flow, process safety and product quality are important issues that should be paid great attentions by enterprise; process monitoring can be employed to solve those two aspects. According to the wide use of the distribution control system in industrial processes, large amounts of data were collected, the multivariate statistical process monitoring (MSPM) methods have been widely applied to solve the fault monitoring.The traditional MSPM methods are limited to Gaussian, linear, stationary and single mode processes. However, in the actual industrial process, the process data is so complicated that its distribution is hardly ascertained. And production processes often have not only a stable working condition, most of the production processes are multi-mode process. This dissertation develops the research based on the predecessor’s work, according to the complex distribution of the process data, the main research contents are listed as follows:(1) Dealing with multi-mode production process, the traditional statistical analysis methods neglect the correlation between modes. In this dissertation, from the global analysis of the multi-mode, the common underlying characteristics information among different modes is extracted by manifold learning algorithm:LLE, which is called common subspace. After that, the specific part of each mode is obtained. The common part is the similar variable correlations over modes, and the specific part is the correlations which are not shared by all modes, different KPCA model are established respectively. By the analysis of monitoring the common part and the specific part respectively, this method can give a better understanding of multimode process and a good monitoring effect. When working mode changes, the common part model and the corresponding specific part model are selected for monitoring. The fused magnesium furnace industrial process monitoring simulation results prove the feasibility of this method.(2) Production data of actual process usually have characteristics of nonlinearity and non-gaussian, principal component analysis only involves the second-order feature of signal data, not considering the higher-order statistical characteristics, so the higher-order redundant information are still likely to exist between the transformed data. Moreover, PCA method only removes the correlation between data and doesn’t give the corresponding analysis of the independence of data, which makes PCA and modified PCA method do not work well in above-mentioned case. KLLE-KICA method is proposed to solve this problem. The new method is applied into the cold rolling continuous annealing process. The simulation result of the process monitoring shows this method can reduce the false-alarms and improve the accuracy of the fault monitoring.更多还原