基于小波变换消噪和盲源信号分离的过程监控方法

针对过程信息不可避免地受噪声污染，提出了一种基于小波变换消噪和盲源信号分离的过程监控方法.该方法首先利用小波变换对过程测量信号消噪，再根据信息最大化准则提取盲源信号，然后利用Parzen窗法建立盲源信号的控制限及相应的过程监控图.通过对一个非等温连续搅拌过程（CSTR）的仿真研究表明，该方法是有效的.此外，为了与传统基于盲源信号分离的过程监控方法做比较，还进行了相应的对比研究.结果表明，对过程测量信息首先进行小波变换消噪能够提高过程的监控性能，减少过程故障的误报率和漏报率，从而进一步证实了方法的有效性.

Method of process monitoring based on blind source separation with denoised information by wavelet transform

In this paper, a new process monitoring method is presented based upon wavelet transform and blind source separation. At first, wavelet transform is employed to de-noise measured signals to remove the process noise. Then blind source separation based on information maximization is used to extract blind source signals of the process. After this, process control limits and monitoring plots are built by estimating the probability distribution of every blind signal by means of Parzen density estimator. For investigating the feasibility of this method, its fault-detection performance is evaluated and compared with other method also based on blind source analysis directly with process information by applying it to a continuous-stirred-tank-reactor process. The results show the superiority of the method presented in this paper over other process monitoring method, which has high false alarms and missing alarms.