Model-Based Fault Diagnosis in Electric Drives Using Artificial Neural Networks

Abstract

A model based fault diagnostics study of the power electronics inverter based electrical drive is proposed. The power electronics inverter is considered to be the weakest link in such a system, hence the focus of the work is initially on fault conditions of the inverter. A faulted model for the inverter and the motor is used to generate various fault condition data, which are then compared against data generated by a normally functioning model. An artificial neural network is used to detect these faults based on features extracted from signals. The technique is viable for quick fault detection, and also the time of a fault. The concepts introduced in the paper can be effectively applied for real-time fault diagnostics in electric and hybrid vehicles, and other applications where electrical drives are used.

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Document Details

Document Type
Technical Report
Publication Date
Dec 08, 2003
Accession Number
ADA576801

Entities

People

  • Bai Fang Zhang
  • Hongbin Jia
  • M. A. Masrur
  • Yi L. Murphey
  • Zhi Hang Chen

Organizations

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

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Computer Programs
  • Computers
  • Electric Vehicles
  • Electronics
  • Engineering
  • Hybrid Electric Vehicles
  • Information Operations
  • Inverters
  • Neural Networks
  • Power Electronics
  • Vehicles

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Fault Tolerant Diagnosis of Black and White Balloon Isolation Tests Using ¥.
  • Integrated Circuit Design and Technology.

Technology Areas

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