Prognosis of Electrical Faults in Permanent Magnet AC Machines using the Hidden Markov Model

Abstract

-Failure prognosis and prediction of future state of operation is important to ensure continued operation and to exercise condition base maintenance. -Most of the work in machines is focused on the diagnosis instead of prognosis. But time to failure, or remaining useful life is important. -Generally prognosis needs large historic data sets to extract fault progression trends, which are not available in most of the cases. The problem under study: -A method for prognosis of electrical failures in a PM synchronous motor is presented. -Transient increased contact resistance faults are investigated. Similar results for turn-turn and turn to frame faults. Objective: Determine the probability of failure at the next step.

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Document Details

Document Type
Technical Report
Publication Date
Nov 10, 2010
Accession Number
ADA577546

Entities

People

  • Elias G. Strangas
  • Selin Aviyente
  • Syed S. Zaidi
  • Wesley G. Zanardelli

Organizations

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

Tags

Communities of Interest

  • Air Platforms
  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Data Sets
  • Determinants (Mathematics)
  • Frequency
  • Heuristic Methods
  • Hidden Markov Models
  • Machine Learning
  • Magnets
  • Maintenance
  • Markov Models
  • Models
  • Observation
  • Permanent Magnets
  • Probability
  • Resistance
  • Statistics
  • Synchronous Motors

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Electrical Engineering
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