Fault Diagnosis for Refrigeration Systems Based on Principal Component Analysis and Support Vector Machine

Abstract

Abstract: 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 multiclass classification problems directly, several classical multiSVMs algorithms were analyzed and compared, and the “One vs others” algorithm was adopted. The hybrid PCASVM 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).

Key words: refrigeration system, fault diagnosis (FD), principal component analysis (PCA), support vector machine (SVM)

CLC Number:

TB66

HAN Hua-1, GU Bo-1, REN Neng-2. Fault Diagnosis for Refrigeration Systems Based on Principal Component Analysis and Support Vector Machine[J]. Journal of Shanghai Jiaotong University, 2011, 45(09): 1355-1361.

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