Rolling bearing fault diagnosis based on information fusion and parallel lightweight convolutional network

Abstract

With the development of technologies such as Internet of Things and big data, the realization of fusion and cross analysis of multi-sensor signals provides the possibility for comprehensive condition monitoring and intelligent fault diagnosis of rolling bearings. Considering the bottleneck that deep neural networks with complex parameters and a single convolution kernel style may cause high computational effort and information loss, an intelligent fault diagnosis method based on information fusion and parallel lightweight convolutional network is proposed. Firstly, a normalized pulse energy kurtosis weighted rule is constructed to enable the fusion of multi-channel vibration signals. What’s more, a novel lightweight convolutional neural network is designed to achieve feature extraction and classification. Finally, a novel nonlinear piecewise activation function is introduced to further improve nonlinear learning ability. The effectiveness and superiority are verified by bearing data sets with different speeds and loads. Compared with other models, our diagnostic framework has better effect and provides a new idea for intelligent fault diagnosis.