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This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time–frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate. [ABSTRACT FROM AUTHOR]

Automatic feature extraction is one of the most advantageous merits of deep neural network (DNN), meanwhile, it is an important part for intelligent bearing fault diagnosis. However, most of fault diagnosis methods based on DNN usually excavate the complex relations from original time sequence signals which only present the fault information in time domain. Convolutional Neural Network (CNN) has demonstrated powerful feature learning capabilities in bearing fault diagnosis and the deeper the diagnosis model is, the better the recognition performance is, which resulted in some problems. In order to enrich the fault information from different views and enhance the discrimination for features learned from diagnosis network, this paper proposed a bearing fault diagnosis method based on multi-domain information fusion and improved residual dense network. The original signal and its transformed signals composed the multi-channel input, which contained more comprehensive information and will benefit the deep learning. Then it designed a residual dense network and introduced the convolution attention mechanism which can discriminate the importance of features further improve the feature extraction capability and efficiency of diagnosis network. Finally, it achieved the fault classification, analyzed the effects of key parameters and compared with other diagnosis to verify the effectiveness by lots of experimental results. [ABSTRACT FROM AUTHOR]