Model-Based Fault Diagnosis in Electric Drive Inverters Using Artificial Neural Network

This paper presents research in model based fault diagnostics for the power electronics inverter based induction motor drives. A normal model and various faulted models of the inverter-motor combination were developed, and voltages and current signals were generated from those models to train an artificial neural network for fault diagnosis. Instead of simple open-loop circuits, our research focuses on closed loop circuits. Our simulation experiments show that this model-based fault diagnostic approach is effective in detecting single switch open-circuit faults as well as post-short-circuit conditions occurring in power electronics inverter based electrical drives.