CNN-Based Feature Fusion Motor Fault Diagnosis

Artificial intelligence fields have been using deep learning in recent years. Due to its powerful data mining capabilities, deep learning has a wide-ranging impact on the diagnosis of motor faults. A method for diagnosing motor faults based on the multi-feature fusion of convolutional neural network (CNN) is presented in this paper. As far as the method is concerned, CNN is used as the basic framework, and the CNN model has been improved. First, the collected vibration and current signals are preprocessed. Second, segmented multi-time window synchronous input is performed on the processed data. In addition, a multi-scale feature extraction process and time series fusion of vibration and current signals subject to synchronous input in the same time window can be performed, which ultimately enables the identification of motor faults with a high degree of accuracy. In order to verify the validity of the proposed fault diagnosis model, an experimental platform for fault simulation was built for the motor, and vibration and current signals of different motor states were collected and verified by experimentation. According to the results of the experiment, the method can effectively combine motor vibration and current signal fault features, and thus motor fault diagnosis can be improved. In comparison with a single signal input, a multi-signal input provides greater accuracy and stability. As compared to other multi-signal feature fusion methods, such a deep learning model is able to extract fault features in a more comprehensive manner, which helps to improve the accuracy of motor fault diagnosis.

Automated summary

This paper presents a method for diagnosing motor faults based on the multi-feature fusion of convolutional neural network (CNN), which improves the accuracy and stability of motor fault diagnosis through various processing methods such as multi-scale feature extraction and time series fusion.