基于小波变换去噪的多元统计投影分析及其在化工过程监控中的应用

引　言化工过程数据的重要特点之一是受噪声污染严重 ,这严重影响了过程信息处理和分析的效果 .例如 ,在运用多元统计过程控制 (MSPC)进行化工过程监控时 ,直接利用这些受到污染的测量信息对过程进行分析 ,必然会导致较大的误差 ,使结果置信度下降 .对于过程故障诊断来说 ,就会产生较高的误报率或漏报率 .为解决这一问题 ,人们运用小波变换对测量信号进行去噪 ,提高了信号的置信度并取得了较　.　好的应用效果[1] .然而 ,通常的小波变换去噪是针对过程测量信号直接进行分频去噪 ,并没有考虑信号间的相关关系 ,因而这一处理方法不能更有效…

MSPA BASED ON PROCESS INFORMATION DENOISED WITH WAVELET TRANSFORM AND ITS APPLICATION TO CHEMICAL PROCESS MONITORING

In industrial processes, measured data are often contaminated by noise, which causes poor performance of some techniques driven by data Wavelet transform is a useful tool to de noise the process information, but conventional transaction is directly employing wavelet transform to the measured variables, which will make the method less effective and more multifarious if there exists lots of process variables and collinear relationships In this paper, a novel multivariate statistical projection analysis (MSPA) based on data de noised with wavelet transform and blind signal analysis is presented, which can detect fault more quickly and improve the monitoring performance of the process The simulation results applying to a double effect evaporator verify higher effectiveness and better performance of the new MSPA than classical multivariate statistical process control(MSPC)