基于平稳性能不确定信息盲源信号提取的过程监控方法

针对工业过程中的信息不一定平稳，提出了一种基于平稳性能不确定信息盲源信号提取的过程监控方法，并利用该方法提取过程盲源信号，采用k-近邻法进行分类，从而实现对过程性能的监控.通过对简单AR(1)过程和双效蒸发过程的仿真研究表明，这种方法是可行的.为了与基于传统独立成分分析(ICA)和多元统计过程控制(MSPC)的过程监控方法相比较，还作了相应的对比研究.结果表明，该方法比基于传统ICA的过程监控方法具有更少的误报率和漏报率，而比基于MSPC的过程监控方法具有更少的误报率，从而说明了该方法不仅是可行的，而且是有效的.

Process monitoring based on blind signal extraction with process information of indeterminate stationariness

Process monitoring based on multivariate statistical projection analysis (MSPA) has successfully been applied to chemical processes. However, measured data are often dealt with as stationary information in industrial processes, which is not a case in fact. In this study, a new process monitoring method based on blind signal extraction and k-nearest classifier is presented, which dose not demand stationary measured data.In order to verify the effectiveness and feasibility of this method, the new process monitoring method was applied to a simple AR(1) process and a double-effect evaporator. The simulation results showed that the process monitoring method presented in this paper had fewer false alarms and missing alarms than that based on independent component analysis (ICA) and fewer false alarms than that based on multivariate statistical process control (MSPC).Therefore, the process monitoring method presented in this paper is more effective and better than conventional process monitoring methods.