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Xie (2020): An improved YOLOv3 model based on skipping connections and spatial pyramid pooling, Systems Science & Control Engineering,

In the online test of bearing fault diagnosis algorithm based on data-driven, 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 the 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 datasets and an experimental platform data verify that the method can achieve high-precision fault diagnosis, and inverted Mel-scale Frequency cepstrum coefficients are effective in solving the problem of inconsistent distribution.

This study proposes a hybrid feature convolutional neural network (HFCNN) model for the complete description of three-dimensional (3D) point cloud features. The HFCNN confers sensitivity to the local, global, and single-point properties simultaneously by a feature vector space expansion. Wherein, a pointwise convolutional network sub-model realises the extraction of the local features by using a pointwise convolutional operator to process point cloud data directly. To consider the global properties of the point cloud, a central-point radiation model is constructed as an input of the feature layer in a nonnetwork form. Meanwhile, the single-point behaviour is characterised by the solo point coordinate information in the network. Within the constructed solo-local-global feature space, i.e. the fusion of single point feature, local feature and global feature, the HFCNN model can handle 3D point cloud data with unstructured and unordered properties. The HFCNN can be directly applied to the point cloud classification and segmentation without the modification of the CNN structure and training procedure. The experimental results have shown the effectiveness of the proposed model in prediction of class labels and point-by-point labels.

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