%A HAN Hua-1, GU Bo-1, REN Neng-2 %T Fault Diagnosis for Refrigeration Systems Based on Principal Component Analysis and Support Vector Machine %0 Journal Article %D 2011 %J Journal of Shanghai Jiao Tong University %R %P 1355-1361 %V 45 %N 09 %U {https://xuebao.sjtu.edu.cn/CN/abstract/article_39766.shtml} %8 2011-09-30 %X Principal component analysis (PCA) was employed to make feature extraction from the vast data pool, and the PCA+SVM (support vector machine) model was established for the fault detection and diagnosis (FDD) of refrigeration systems. Considering that SVM can not be used to solve multiclass classification problems directly, several classical multiSVMs algorithms were analyzed and compared, and the “One vs others” algorithm was adopted. The hybrid PCASVM FDD model was presented and validated by the historical data from specially designed experiments. The results show that it can isolate normal from faulty modes (detection) and has a diagnostic rate of no less than 98.57%, which is better than the SVM model without PCA; model training is about 130—350 times faster than the latter; it also has better performance on dealing with small sample problem than BP neural network with higher diagnostic rate and much less training time (about 1/240).