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A deep convolutional neural network approach with information fusion for bearing fault diagnosis under different working conditions

In recent years, deep learning-based fault diagnosis methods have drawn lots of attention. However, for most cases, the success of machine learning-based models relies on the circumstance that training data and testing data are under the same working condition, which is too strict for real implementation cases. Combined with the features of robustness of deep convolutional neural network and vibration signal characteristics, information fusion technology is introduced in this study to enhance the feature representation capability as well as the transferability of diagnosis models. With the basis of multi-sensors and narrow-band decomposition techniques, a convolutional architecture named fusion unit is proposed to extract multi-scale features from different sensors. The proposed method is tested on two data sets and has achieved relatively higher generalization ability when compared with several existing works, which demonstrates the effectiveness of our proposed fusion unit for feature extraction on both source task and target task.


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