A Novel Decentralized Weighted ReliefF-PCA Method for Fault Detection

The decentralized weighted ReliefF-PCA (DWRPCA) method is proposed to improve the performance of principal component analysis (PCA) for fault detection. The improved ReliefF-PCA algorithm is used to select the principal components instead of the traditional cumulative percent variance (CPV) criterion, so that the important information contained in the small variance is considered. The sub-models for different types of faults which are being considered the influence weights of process variables and faults are established respectively to obtain the decentralized weighted model. The Bayesian Information Criterion is adopted to integrate different types of faults for a unified monitoring index. The case study of a numerical example and the Tennessee Eastman process illustrate the effectiveness of the proposed method.