Study on fault diagnosis of ultra-low-speed rolling bearings based on full vector sound spectrogram

By exploring the mapping relationship between the multi-directional data and fault characteristics of bearings, a time-frequency analysis method for considering the multi-directional acoustic emission (AE) data of bearings is proposed. Firstly, using the full vector spectrum (FVS) theory, the full vector sound spectrogram of the dual-channel AE signal of a bearing is extracted to enhance the representation of the fault state using time-frequency characteristics. Then, the obtained full vector sound spectrogram is transformed into a specific size as the input feature map and a convolutional neural network (CNN) classifier model is established. Next, the Softmax classifier is used to classify the bearing faults in order to realise the intelligent fault diagnosis of an ultra-low-speed rolling bearing. The comparison of the different models shows that the average recognition accuracy using the full vector sound spectrogram CNN model can reach 95.61%, which is better than the other three methods. The feature extraction using the full vector sound spectrogram feature analysis method has a high degree of recognition for bearing faults in an ultra-low-speed state and can provide high accuracy and stability under noisy conditions.