Bearing Fault Diagnosis Based on Frequency Subbands Feature Extraction and Multibranch One-Dimension Convolutional Neural Network

The rolling bearing is one of the important parts of rotating machinery, while the degree of dependence on the machine is becoming heavier nowadays. Therefore, it is always necessary to monitor its operating status and diagnose faults. To better analyze the bearing vibration signal from the time domain and frequency domain and reduce information loss, we propose a model that decomposes the original bearing vibration signal with a length of 1024 by a two-layer wavelet packet. For the analysis, four low-frequency and high-frequency feature vectors of a length of 1024 are obtained as the input for the analysis model. The proposed model uses frequency subbands to automatically extract features from network input and then fuse the features. The accuracy of the model on a single load on the Case Western Reserve University (CWRU) dataset is 98-100%, which shows the diagnostic effect is satisfactory.