Fault Diagnosis Using Dynamic Principal Component Analysis and GA Feature Selection Modeling for Industrial Processes

With the continuous expansion of industrial production scale, most of the chemical process variables are nonlinear, multi-modal and dynamic. For some traditional multivariate statistical monitoring and fault diagnosis algorithms, such as principal component analysis (PCA), the premise of its application is that the process data is time-independent. To this end, a dynamic principal component analysis (DPCA) method is proposed. However, since the input matrix of DPCA fault diagnosis needs to add an augmented matrix to the original data matrix, the number of eigenvalues of the augmented matrix is too large and there are many redundant eigenvectors. Therefore, this paper proposes a fault diagnosis and monitoring algorithm combining feature selection and DPCA, which considers the dynamic characteristics of multivariate data and reduces the dimension of the input matrix. At present, the average modeling and diagnostic accuracy of PCA-based fault diagnosis on T2 statistic is 65.49%, and that on Q statistic is 76.78%. The average modeling and diagnostic accuracy of fault diagnosis based on DPCA on T2 statistic is 63.17%, and the average modeling and diagnostic accuracy on Q statistic is 83.65%. Finally, through a TE simulation process, this paper proves that the accuracy is greatly improved when using the method proposed in this paper compared with PCA and DPCA.