Data Dimensionality Reduction Based on Multivariate Statistical Analysis

Principal Component Analysis (PCA) is a data dimensionality reduction method based on multivariate statistical analysis. It utilizes the correlations between process variables to establish a principal component model under normal operating conditions. By examining the deviation degree of new data samples from this model, anomalies and faults can be detected. The implementation typically involves eigenvalue decomposition of the covariance matrix to identify dominant patterns in the data.