基于改进多尺度主元分析的丙烯聚合过程监测与故障诊断

针对工业过程故障检测问题，提出了一种改进多尺度主元分析方法。首先针对过程数据所具有的随机性、非平稳性及含有大量噪声等特点，提出了一种改进小波变换阈值去噪方法，移除原始过程数据中的大部分高频随机噪声，提高数据的置信度，然后应用小波多尺度分解将每个变量依次分解成逼近系数和多个尺度的细节系数，在各个尺度矩阵建立相应的主元分析模型，以模型统计量控制限为阈值，对小波系数重构得到综合尺度主元分析模型。将该改进多尺度主元分析方法应用于丙烯聚合过程监测与故障诊断研究中，研究结果表明，与传统多尺度主元分析相比，改进多尺度主元分析减少了误报率和漏报率，提高了过程监测与故障诊断的精度。

Process monitoring and fault diagnosis of propylene polymerization based on improved multiscale principal component analysis

In order to handle the problem of fault detection for industrial process,an improved multiscale principal component analysis(MSPCA)is proposed.Firstly,considering the nonstationary and random nature of data in the process industry which contains different noises inevitably,an improved wavelet threshold denoising method which combines multiple wavelet transform with a new threshold function based on the characteristics of wavelet analysis is proposed.The data collected from the industry condition are processed by means of the improved wavelet threshold denoising method.Using wavelets,the individual variable is decomposed into approximations and details at different scales.Contributions from each scale are collected in separate matrices,and a PCA model is then constructed to extract correlation at each scale.According to the simulation of propylene polymerization,and comparing the improved MSPCA with traditional MSPCA,it shows that the improved MSPCA has enhanced the accuracy of process monitoring and fault diagnosis.