- 期刊
Rotating Machine Fault Diagnosis Based on KPCA and Optimized BPNN-KNN Model
並列摘要
In order to effectively recognize the rotating machine fault, a new method is proposed. Firstly, the gathered vibration signals are decomposed by the empirical mode decomposition (EMD), the corresponding intrinsic mode functions (IMF) are got. Then, Shannon entropy of the IMFs is used as the original features. But the extracted features have the problems of high dimension and redundancy serious. So, the KPCA is introduced to extract the characteristic features. The characteristic features are inputted to the BPNN-KNN model to train and construct the fault diagnosis model, the rotating machine fault condition identification is realized. The running states of a normal inner race and several inner races with different degree of fault were recognized, the results validate the effectiveness of the proposed algorithm.
延伸閱讀
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