Multi-featured Fault Diagnosis for Rolling Bearings Based on Support Vector Machines
The commonly used research method for bearing diagnosis is feature extraction of vibration signals of rolling bearings,and the diagnostic accuracy of single dimensional signal feature extraction is not high.Focusing on this pain point,the support vector machine(SVM)is integrated on the basis of multi-features for rolling bearing fault diagnosis.The method firstly reduces the noise by wavelet transform and extracts features from the vibration signals of the inner ring,the outer ring and the rolling element generated by rolling bearings,and then screens the appropriate wavelet basis functions,secondly extracts the time domain,frequency domain and IMF energy features based on the ensemble empirical modal decomposition method from the noise reduced vibration signals.The results show that com-pared with the traditional feature extraction and classification discrimination methods,the accuracy of rolling bearing fault diagnosis by SVM relies on both multi-dimensional and multi-domain fusion feature sets has achieved 100%——It has good classification capability.
Rolling bearingsFault diagnosisWavelet transformMulti-featuresSupport vector machines
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