Dynamic PCA for Fault Diagnosis

Dynamic Principal Component Analysis, commonly referred to as DPCA, serves as an effective methodology for fault diagnosis implementation. It operates by analyzing system data and performing pattern recognition based on these datasets, enabling accurate fault detection and diagnosis. The DPCA approach involves constructing an augmented data matrix with time-lagged variables, followed by conventional PCA implementation to capture dynamic relationships within the system. This method finds applications across various domains including industrial production, mechanical equipment, and electronic circuits. Its implementation typically requires data preprocessing, covariance matrix computation, eigenvalue decomposition, and statistical limit calculation using T² and Q statistics. The widespread applicability of DPCA has demonstrated significant achievements in practical engineering applications. By employing the DPCA methodology, we can enhance both the accuracy and efficiency of fault diagnosis, thereby improving system reliability and stability through continuous monitoring and early anomaly detection.