Fault Diagnosis Using Shallow Neural Networks for Voltage Source Inverters in Motor Drives

This paper introduces a novel fault diagnosis approach for two-level Voltage Source Inverters in Motor Drives, using Shallow Neural Networks. A key feature of this study is the implementation of three different Classifiers: two are designed to both detect and diagnose faults, while the third is specialized in fault detection and localization. The strategic adoption of Shallow Neural Networks plays a pivotal role in substantially reducing computational demands while enabling the direct utilization of raw, normalized data obtained from both simulation and experimental activities. The research includes a comparative analysis of three distinct shallow neural network architectures against other machine learning methods to underscore their effectiveness. Specifically, the use of shallow Long Short Term Memories networks is a pioneering effort in the field of power converter fault detection, especially for their reduced memory requirement (one hidden layer with 64 units). The final results show that the average time per observation for the localization classifier(7 Class-Classifier) across the entire test set is equal to 39 $\mu$s. This efficiency is coupled with promising metrics, including a precision, recall and F1-score of 94.50%, 94.24%, and 94.35%, respectively.