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Parametric signal processing approach

Parametric signal processing approach

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Signal Processing for Fault Detection and Diagnosis in Electric Machines and Systems — Recommend this title to your library

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This chapter addresses the issue of condition monitoring based on MCSA using parametric spectral estimation techniques and detection theory. This approach is used for fault characteristics estimation. Then, generalized likelihood ratio test (GLRT) is used for automatic decision-making. The proposed fault detection approach uses fault frequency signature bins and amplitude estimators, and a fault decision module based on statistical tools. MLE is used for fault characteristics computation. Then, composite hypothesis testing is used as a decision module. The main objective is to discriminate the healthy induction motor from a faulty one. Finally, a fault severity measurement criterion is proposed and demonstrated for several induction motor fault detection.

Chapter Contents:

  • 1.1 Fault effects on intrinsic parameters of electromechanical systems
  • 1.1.1 Main failures and occurrence frequency
  • 1.1.2 Origins and consequences
  • 1.1.3 Condition-based maintenance
  • 1.1.3.1 Fault detection methods
  • 1.1.3.2 Fault effects on stator currents
  • 1.1.4 Motor current signature analysis
  • 1.1.4.1 Fault frequency signatures
  • 1.1.4.2 Stator currentAM/FM modulation
  • 1.2 Fault features extraction techniques
  • 1.2.1 Introduction
  • 1.2.2 Stator current model under fault conditions
  • 1.2.2.1 Model assumptions
  • 1.2.2.2 Stator current modelling
  • 1.2.3 Non-parametric spectral estimation techniques
  • 1.2.4 Subspace spectral estimation techniques
  • 1.2.5 ML-based approach
  • 1.2.5.1 Exact ML estimates
  • 1.2.5.2 Approximate ML estimates
  • 1.2.5.3 Model order selection
  • 1.3 Fault detection and diagnosis
  • 1.3.1 Artificial intelligence techniques briefly
  • 1.3.2 Detection theory-based approach
  • 1.3.2.1 Background on binary hypothesis testing
  • 1.3.2.2 GLRT for fault detection
  • 1.3.3 Simulation results
  • 1.3.3.1 Estimation performance
  • 1.3.3.2 Fault detection performance
  • 1.4 Some experimental results
  • 1.4.1 Experimental set-up description
  • 1.4.2 Eccentricity fault detection
  • 1.4.3 Bearing fault detection
  • 1.4.4 Broken rotor bars fault detection
  • 1.5 Conclusion
  • References

Inspec keywords: induction motors; fault diagnosis; decision making; spectral analysis; condition monitoring; machine testing; maximum likelihood estimation

Other keywords: healthy induction motor; fault detection approach; composite hypothesis testing; fault characteristics estimation; condition monitoring; statistical tools; generalized likelihood ratio test; fault severity measurement criterion; amplitude estimators; fault decision module; fault frequency signature bins; detection theory; MCSA; automatic decision-making; parametric spectral estimation techniques; decision module; parametric signal processing

Subjects: Other topics in statistics; Asynchronous machines; Signal processing and detection

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