Bearing fault diagnosis based on inverted Mel-scale frequency cepstral coefficients and deformable convolution networks

In the real-time test fault diagnosis algorithm based on deep learning, it is difficult to guarantee that the training and testing data come from the same time series distribution. Inconsistent distribution will lead to a decline in diagnostic performance. In addition, the convolutional neural network is limited by the fixed shape of its convolution kernel, which makes it difficult to fully extract the spatial constraint features between fault data. To solve the above problems, this paper proposes a bearing fault diagnosis method based on inverted Mel-scale frequency cepstrum coefficients and deformable convolution networks. The core of traditional Mel-scale frequency cepstrum coefficients is to construct a non-uniformly distributed frequency-domain filter bank. It is characterized by the dense distribution of low-frequency regions and the sparse distribution of high-frequency regions. Considering that the features that can well characterize fault information are concentrated in the high-frequency part, we reconstruct the traditional Mel-scale frequency cepstrum coefficients filter bank and propose a feature extraction method of inverted Mel-scale frequency cepstrum coefficients. This method can obtain the frequency-domain characteristics of bearing vibration signals, highlight the fault information contained in the high-frequency region, and reduce the influence of time series distribution inconsistency between training samples and testing samples on the diagnosis accuracy. In order to further improve the spatial discrimination between different fault categories, the deformable convolution networks model is introduced to extract the spatial distribution information of fault features and improve the accuracy of fault diagnosis. Finally, two public data sets and data from an experimental platform verify that the method can achieve high-precision fault diagnosis, and that inverted Mel-scale Frequency cepstrum coefficients are effective in solving the problem of inconsistent distribution.

Automated summary

This paper proposes a bearing fault diagnosis method based on inverted Mel-scale frequency cepstrum coefficients and deformable convolution networks. By reconstructing the traditional Mel-scale frequency cepstrum coefficients filter bank, the frequency-domain characteristics of bearing vibration signals are obtained, the fault information contained in the high-frequency region is highlighted, and the influence of time series distribution inconsistency between training samples and testing samples on the diagnosis accuracy is reduced. The introduction of deformable convolution networks model further improves the spatial discrimination between different fault categories and improves the accuracy of fault diagnosis.