Fault Diagnostics in Electric Drives Using Machine Learning

Abstract

Electric motor and power electronics based inverter are the major components in industrial and automotive electric drives. In this paper we present a fault diagnostics system developed using machine learning technology for detecting and locating multiple classes of faults in an electric drive. A machine learning algorithm has been developed to automatically select a set of representative operating points in the (torque, speed) domain, which in turn is sent to the simulated electric drive model to generate signals for the training of a diagnostic neural network, "Fault Diagnostic Neural Network" (FDNN). We presented our study on two different neural network systems and show that a well-designed hierarchical neural network system is robust in detecting and locating faults in electric drives.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2006
Accession Number
ADA524867

Entities

People

  • M. A. Masrur
  • Yi L. Murphey
  • Zhihang Chen

Organizations

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

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Circuits
  • Classification
  • Computer Vision
  • Computing System Architectures
  • Data Sets
  • Detection
  • Electric Motors
  • Induction Motors
  • Machine Learning
  • Motors
  • Network Architecture
  • Neural Networks
  • Power Electronics
  • Short Circuits
  • Simulations

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Electrical Engineering
  • Fault Tolerant Diagnosis of Black and White Balloon Isolation Tests Using ¥.

Technology Areas

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