Data Dimensionality Reduction Based on Multivariate Statistical Analysis

In industrial process control, Principal Component Analysis (PCA) serves as a widely adopted data dimensionality reduction technique. Grounded in multivariate statistical analysis, PCA leverages inter-correlations among process variables to construct a principal component model representing normal operational conditions. This model enables better understanding of equipment and process behaviors, thereby facilitating improved problem diagnosis and handling. When new data samples become available, their deviation from the established PCA model can be evaluated to identify potential anomalies and faults. Implementation typically involves standardizing data, computing the covariance matrix, performing eigenvalue decomposition to obtain principal components, and projecting data onto the reduced-dimensional space. PCA proves to be an invaluable tool for enhancing comprehension and control of industrial processes through systematic data analysis.