Deep Learning Approach for open switch Fault Diagnosis in Matrix Frequency Converter

摘要

This document proposes a new method for detecting and locating open circuit faults in a matrix frequency converter (MC) based on the technique of pattern recognition by neural networks. The converter input and output current signals are used for this purpose. For this, a database of current signals under healthy conditions and defective for different operating conditions was established. After transforming these signals into a Concordia lair, a process of deep learning by a convolutional neural network was carried out. To verify the robustness of our proposed approach, a simulation of a MC system with a defective power electronic switch supplying an asynchronous motor controlled by DTC-SVM under different conditions of torque and speed was developed. The diagnostic results demonstrate the feasibility and effectiveness of the proposed method. It made it possible to locate the faulty switch precisely and quickly