An enhanced DPCA fault diagnosis method based on hierarchical cluster analysis

A large amount of data generated in industrial processes exhibit multi-modal, nonlinear, time-domain correlation, and other characteristics. This poses great difficulty for the traditional principal component analysis (PCA) method since it requires that the input data need to conform to the Gaussian distribution. However, the data may have autocorrelation, that is, the data at the current moment will be affected by the past data. To this end, this paper proposes an enhanced dynamic principal component analysis (DPCA) method based on hierarchical clustering analysis. On the basis of the DPCA algorithm, the idea of data classification and enhanced training is used to strengthen the training of the dimensionality reduction matrix. Then, calibration, on-line monitoring, and fault diagnosis of process data can be conducted. Finally, this paper demonstrates that the performance of the proposed method is greatly improved compared with PCA and DPCA through the Tennessee Eastman process system.