Shulong Zhuo
ABSTRACT
The traditional bearing fault type recognition algorithm has the advantages of high recognition accuracy and good recognition performance when dealing with pure bearing fault data, but its recognition performance drops sharply in the actual noise environment. In order to effectively improve the reliability and accuracy of bearing fault type recognition, combined with the huge advantages of deep learning algorithm in the recognition field, one bearing fault type recognition technology based on ResNet and FPN. This Combined Neural Network is proposed to be applied in the actual working condition. This paper utilizes the Case Western Reserve University CWRU dataset. First, MATLAB is used to simulate the original bearing data through the channel. And the dataset under various signal-to-noise ratios are constructed accordingly. Then, the combined neural network constructed in this paper is trained and tested with the constructed data samples. After original dataset and constructed dataset been decomposed and reorganized by traditional empirical mode decomposition (EMD), multi-dimensional feature extraction and fault type recognition are completed through convolutional neural network (CNN). Finally, the proposed combined neural network algorithm is compared with the traditional bearing fault type recognition algorithm. The experimental results show that the recognition performance of RFNet is far superior to the traditional recognition algorithm under the actual working conditions.
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Index Terms
- Research on Bearing Fault Type Recognition Technology Based on RFNet Combined Neural Network
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