Bearing Fault Diagnosis Program with Data and Running Results

Application Context: Bearing fault diagnosis program based on PCA technology, complete with data and operational results! Key Technology: Principal Component Analysis (PCA) is a multivariate statistical method that transforms numerous correlated variables (e.g., P indicators) into a new set of uncorrelated composite indicators. This technique examines inter-variable correlations to reveal internal structures through fewer principal components, preserving maximum original variable information while ensuring mutual independence. Mathematically, this involves linear combinations of original P indicators to form new synthetic indicators. The classical approach selects F1 (the first linear combination) as the primary component, implemented algorithmically through eigenvalue decomposition of covariance matrices.