Wavelet leaders multifractal features based fault diagnosis of rotating mechanism

Abstract A novel method based on wavelet leaders multifractal features for rolling element bearing fault diagnosis is proposed. The multifractal features, combined with scaling exponents, multifractal spectrum, and log cumulants, are utilized to classify various fault types and severities of rolling element bearing, and the classification performance of each type features and their combinations are evaluated by using SVMs. Eight wavelet packet energy features are introduced to train the SVMs together with multifractal features. Experiments on 11 fault data sets indicate that a promising classification performance is achieved. Meanwhile, the experimental results demonstrate that the classification performance of the SVMs trained with eight wavelet packet energy features in tandem with multifractal features outperforms that of the SVMs trained only with wavelet packet energy features, time domain features, or multifractal features, and it is also superior to that of wavelet packet energy features in tandem with time domain features, or multifractal features combined with time domain features. The feature selection method based on distance evaluation technique is exploited to select the most relevant features and discard the redundant features, and therefore the reliability of the diagnosis performance is further improved.

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