Recent advances in modeling and online detection of stator interturn faults in electrical motors

  • Gandhi A
  • Corrigan T
  • Parsa L
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Abstract

Online fault diagnosis plays a crucial role in providing the required fault tolerance to drive systems used in safety-critical applications. Short-circuit faults are among the common faults occurring in electrical machines. This paper presents a review of existing techniques available for online stator interturn fault detection and diagnosis (FDD) in electrical machines. Special attention is given to short-circuit-fault diagnosis in permanent-magnet machines, which are fast replacing traditional machines in a wide variety of applications. Recent techniques that use signals analysis, models, or knowledge-based systems for FDD are reviewed in this paper. Motor current is the most commonly analyzed signal for fault diagnosis. Hence, motor current signature analysis is a topic of elaborate discussion in this paper. Additionally, parametric and finite-element models that were designed to simulate interturn-fault conditions are reviewed.

Author-supplied keywords

  • Analytical model
  • artificial intelligence (AI)
  • condition monitoring
  • fault diagnosis
  • fault tolerance
  • feature extraction
  • induction machines
  • permanent-magnet (PM) machines
  • turn fault

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Authors

  • Arun Gandhi

  • Timothy Corrigan

  • Leila Parsa

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