A sparse PCA for nonlinear fault diagnosis and robust feature discovery of industrial processes

Pearson’s correlation measure is only able to model linear dependence between random variables. Hence, conventional principal component analysis (PCA) based on Pearson’s correlation measure is not suitable for application to modern industrial processes where process variables are often nonlinearly related. To address this problem, a non-parametric PCA model is proposed based on nonlinear correlation measures, including Spearman’s and Kendall tau’s rank correlation. These two correlation measures are also less sensitive to outliers comparing to Pearson’s correlation, making the proposed PCA a robust feature extraction technique. To reveal meaningful patterns from process data, a generalized iterative deflation method is applied to the robust correlation matrix of the process data to sequentially extract a set of leading sparse pseudo-eigenvectors. For online fault diagnosis, the T2 and SPE statistics are computed and analyzed with respect to the subspace spanned by the extracted pseudo-eigenvectors. The proposed method is applied to two industrial case studies. Its process monitoring performance is demonstrated to be superior to that of the conventional PCA and is comparable to those of Kernel PCA and kernel independent component analysis (KICA) at a lower computational cost. The proposed PCA is also more robust in sparse feature extraction from contaminated process data.