经验模式分解模糊特征提取的支持向量机混合诊断模型

为解决机械故障小样本模式识别问题，有效地提高分类的准确率，提出了一种基于经验模式分解模糊特征提取的支持向量机混合诊断模型．该模型通过对信号进行经验模式分解，提取信号的本征模式分量并转化为模糊特征向量，对机器故障进行诊断，然后将模糊特征向量输入到多分类的支持向量机中，实现了对机器不同故障类型的识别．将该模型应用于汽轮发电机组的3种工作状态的识别中，测试结果表明，同原有的未经过任何特征提取以及经过小波包模糊特征提取的2种多分类支持向量机方法相比，该模型将分类准确率从原有的53．33％和86．67％提高到100％，有效地改善了分类的准确性．同时，该模型还为汽轮发电机组的故障确诊提供了有力依据．

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.