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A novel fault diagnosis method based on Improved Singular Value Decomposition (SVD), S-transformation and Improved Convolutional Neural Networks (ICNN) is proposed for the non-stationary, nonlinear, interfered by strong background noise and difficult feature extraction problems of rolling bearing vibration signal of the road heading machine. Firstly, the original signal is constructed into a Hankel matrix which was decomposed by SVD. The effective singular values are selected according to the curvature spectrum of the singular values for signal recon-struction, and the reconstructed signals are transformed by S to generate the feature map, which is input into ICNN adaptive feature extraction for the fault identification. Secondly, the im-proved convolutional neural network uses VGG16 as a Bottleneck structure, introduces the bot-tleneck structure, selects input data with different sizes for feature extraction, adds Fine Tune on the basis of ICNN, and finally realizes fault classification and recognition through network pa-rameter adjustment. The proposed method is applied to the fault diagnosis of road heading ma-chine rolling bearings, and the accuracy rate is 98.2%, which is 9.55% higher than the classic VGG16 model.

The roadheader is a core piece of equipment for underground mining. The roadheader bearing, as its key component, often works under complex working conditions and bears large radial and axial forces. Its health is critical to efficient and safe underground operation. The early failure of a roadheader bearing has weak impact characteristics and is often submerged in complex and strong background noise. Therefore, a fault diagnosis strategy that combines variational mode decomposition and a domain adaptive convolutional neural network is proposed in this paper. To start with, VMD is utilized to decompose the collected vibration signals to obtain the sub-component IMF. Then, the kurtosis index of IMF is calculated, with the maximum index value chosen as the input of the neural network. A deep transfer learning strategy is introduced to solve the problem of the different distributions of vibration data for roadheader bearings under variable working conditions. This method was implemented in the actual bearing fault diagnosis of a roadheader. The experimental results indicate that the method is superior in terms of diagnostic accuracy and has practical engineering application value.