A New Method for Inverter Diagnosis of Electric Locomotive Using Adversarial Neural Networks

Abstract

In order to improve the fault diagnosis accuracy of the electric locomotive inverter, this article combines the adversarial neural network to construct the electric locomotive inverter diagnosis system. Moreover, at the data level, this article compares and analyzes three methods of data expansion based on single-sample processing, data expansion based on image front and background separation, and data expansion based on an adversarial neural network. In addition, this article adopts a new feature extractor and increases the penalty cost of small samples being misclassified. Finally, this article uses the LBP operator to extract the image texture features to distinguish and detect the different shapes of the rotor windings and build an intelligent system to verify the effect of the proposed system model. The experimental research shows that the inverter diagnosis system for electric locomotives based on the proposed adversarial neural network has a good practical effect.