A new incipient fault monitoring method based on modified principal component analysis

摘要

A novel multivariate statistical process monitoring (MSPM) method based on modified principal component analysis (PCA) is proposed to solve the low detection rate problem of incipient fault. In this modified PCA, on the basis of normal PCA model, the columns of loading matrix are reordered by mutual information between different statistic component matrices and training data. Then, instead of cumulative percent variance criterion, the principal component subspace is selected according to the largest mutual information. The selected PC subspace can maximally reflect fault characteristics into a new statistical index. Besides, a detection index based on the sliding average control chart statistic is proposed, which eliminates the effect of noise by proper averaging of the most recent samples, greatly improving the ability and accuracy of fault detection. The case study of a numerical simulation and Tennessee Eastman process shows that the proposed method can effectively detect incipient fault.