基于核主元分析与核密度估计的非线性过程故障监测与识别

在针对将核主元分析(kernel principal components analysis, KPCA)与基于高斯分布的控制限(control limits, CLS)相结合会降低其性能的问题, 提出了一种基于核主元分析与核密度估计(kernel principal components analysis-kernel density estimation, KPCA-KDE)相结合的非线性过程故障监测与识别方法. 该方法采用核密度估计(kernel density estimation, KDE)技术来估计基于KPCA的非线性过程监控的CLS. 通过研究KPCA和KPCA-KDE所有20个故障的检出率发现, 与相应的基于高斯分布的方法进行比较, KDE具有较高的故障检出率; 此外, 基于KDE的检测延迟等于或低于其他方法. 通过改变带宽和保留的主元数量进行故障检测, KPCA记录的FAR值较高, 相反, KPCA-KDE方法仍然没有记录任何假报警. 在田纳西伊斯曼过程(Tennessee Eastman, TE)上的应用表明, KPCA-KDE比基于高斯假设的CLS的KPCA在灵敏度和检测时间上都具有更好的监控性能.

Nonlinear Process Fault Identification and Detection Based on KPCA-KDE

The combination of kernel principal components analysis (KPCA) and control limits (CLS) based on Gaussian distribution will undermine the performance. The fault detection and identification method for nonlinear process based on kernel principal components analysis-kernel density estimation (KPCA-KDE) is proposed. kernel density estimation (KDE) technology is adopted to estimate the CLS based on KPCA for nonlinear process monitoring. According to the detection rate of all 20 faults in KPCA and KPCA-KDE, KDE has a higher fault detection rate than the corresponding method based on Gaussian distribution. In addition, KDE-based detection delay is equal to or lower than other methods. By changing the bandwidth and the number of reserved pivots during the fault detection, KPCA records a larger FAR while the KPCA-KDE does not record any false alarms. The application on the Tennessee Eastman (TE) process shows that KPCA-KDE has better monitoring performance in sensitivity and detection time than KPCA based on Gaussian CLS.