Research and Application of Motor Fault Diagnosis Algorithm Based on Neural Network

The increasing degree of industrial automation makes electric motors play an important role as key power sources in various production lines, so their health monitoring and fault diagnosis are particularly important. The aim of this study is to develop a neural network-based motor fault diagnosis algorithm to accurately identify and predict potential motor faults through deep learning techniques. In this paper, we first collect the operation data of the motor under normal operation and various fault states, including multiple parameters such as current, voltage and temperature, and then use these data to train the deep neural network model so that it can learn the characteristics and patterns of motor faults. Techniques such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are used for model training to enhance the processing capability of time series data and the efficiency of fault feature extraction. In the model validation stage, the advantages of the neural network model in fault detection accuracy and diagnostic speed are demonstrated by comparing it with the traditional fault diagnosis methods. The diagnostic performance of rotor imbalance is the best, with a test accuracy as high as 0.95 and a fault detection rate of 0.93, while the false alarm rate is only 0.04. The neural network-based fault diagnosis algorithm adopted in this paper not only improves the diagnostic accuracy and efficiency, but also facilitates the effective support of the intellectualization and automation of industrial maintenance, reduces the unplanned downtime, and guarantees the safety of production and economic benefits.