PCA-SVM-Based Automated Fault Detection and Diagnosis (AFDD) for Vapor-Compression Refrigeration Systems

To improve the classification accuracy and reduce the training and classifying time, a novel automated fault detection and diagnosis (AFDD) strategy is proposed for vapor-compression refrigeration systems, which combines principle component analysis (PCA) feature extraction technology and the “one to others” (binary-decision-tree-based) multiclass support vector machine (SVM) classification algorithm. Eight typical faults were artificially introduced into a refrigeration system in the laboratory, and tests for normal and faulty conditions were carried out over a −5°C~15°C (23°F~59°F) evaporating temperature and a 25°C~60°C (77°F~140°F) condensing temperature. The data obtained for 16 variables are first preprocessed by PCA to get four comprehensive features (principle components) that account for over 85% of the cumulative percent value (CPV); the new sample data are then randomly split into training (70%) and testing (30%) sets as the input of an eight-layer SVM classifier for AFDD. Results show that the...