Quality-Related Process Monitoring Based on Improved Kernel Principal Component Regression

To date, quality-related multivariate statistical methods are extensively used in process monitoring and have achieved admirable effects. However, most of them contain recursive processes, which result in higher time complexity and are not suitable for increasingly complex industrial processes. Therefore, this paper embeds singular value decomposition (SVD) into the kernel principal component regression (KPCR) to accomplish Quality-related process monitoring with a lower computational cost. Specifically, the kernel technique is devoted to map the original input into the higher dimensional space to boost the nonlinear ability of the principal component regression (PCR), and then the KPCR is employed to capture the correlation between the input kernel matrix and the output matrix. At the same time, the kernelized input space is decomposed into two orthogonal quality-related and quality-unrelated spaces by SVD, and the statistics of the two spaces are calculated to detect the faults respectively. Compared with other multivariate statistical methods, it has the following advantages: 1) A quality-related kernel principal component analysis (QR-KPCR) algorithm is proposed. 2) Compared with partial least squares method, the recursive process is omitted and the training time is shortened. 3) The model is more concise and the fault detection process is faster. 4) By contrast with other multivariate statistical process monitoring, it has a higher fault detection rate. Experimental results on a widespread example and an industry benchmark verify the effectiveness and reliability of the proposed method.