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

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4 Author(s)
Masrur, M.A. ; US Army RDECOM-TARDEC, Warren, MI ; Zhihang Chen ; Zhang, B. ; Murphey, Y.L.

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.

Published in:

Power Engineering Society General Meeting, 2007. IEEE

Date of Conference:

24-28 June 2007

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