Intelligent fault diagnosis of multi-sensor rolling bearings based on variational mode extraction and a lightweight deep neural network

After a rolling bearing failure in an industrial complex environment, the vibration signals collected by the sensors can easily be corrupted by a wide range of noise information, affecting the effectiveness of the feature extraction process. Although deep learning models can extract fault features better, most of the models currently used have complex structures with many parameters and cannot be deployed in embedded environments. In this paper, we propose an intelligent fault diagnosis method combining variational mode extraction (VME) with lightweight deep neural networks, which has the advantages of anti-noise robustness and model lightweight. Firstly, VME is used to process the vibration signals of different sensors to obtain the required modal component signals and convert them into greyscale images. Subsequently, the improved lightweight deep neural network Bypass-SqueezeNet is used for fault diagnosis. Several experiments are conducted on the experimental dataset, and the final experimental results prove that the method proposed in this paper possesses more satisfactory diagnostic performance.