Hybrid diagnosis model of support vector machine based on fuzzy feature extraction with empirical mode decomposition

To solve the small-sample pattern recognition problem of mechanical equipment fault and improve classification ability, a new hybrid diagnosis model of support vector machine (SVM) based on fuzzy feature extraction with empirical mode decomposition (EMD) is proposed, where these intrinsic mode components are extracted with EMD from original signals and converted into fuzzy feature vectors, and then the mechanical fault can be diagnosed. The extracted fuzzy feature vectors are input into the multi-classification SVM to detect the different abnormal cases. This model is applied to the classification of turbo-generator set under 3 operating conditions. Testing results show that the classification accuracy of the proposed model (100% classification success rate) is greatly improved compared with the SVM without feature extraction (53.33% classification success rate) and with the SVM extracting the fuzzy feature from wavelet packets (86.67% classification success rate), and the faults of turbo-generator set can be correctly and rapidly diagnosed.