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

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Dec 05, 2006
Accession Number
ADA490854

Entities

People

  • Abul Masrur
  • Baifang Zhang
  • Hongbin Jia
  • Yi-lu Murphey
  • Zhihang Chen

Organizations

  • United States Army Tank Automotive Research, Development and Engineering Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Circuits
  • Electrical Engineering
  • Electronics
  • Engineering
  • Induction Motors
  • Information Operations
  • Inverters
  • Motors
  • Networks
  • Neural Networks
  • Power Converters
  • Power Electronics
  • Power Engineering
  • Short Circuits

Fields of Study

  • Engineering

Readers

  • Fault Tolerant Diagnosis of Black and White Balloon Isolation Tests Using ¥.
  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Microelectronics
  • Microelectronics - Microelectromechanical Systems