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Kullback–Leibler divergence for incipient fault diagnosis

Kullback–Leibler divergence for incipient fault diagnosis

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This chapter discusses the issue of incipient fault detection and diagnosis (FDD). After a general introduction, the requirements for FDD methods are defined under the three criteria of robustness, sensitivity, and simplicity. A methodology of FDD is also introduced in four main steps: modelling, preprocessing, features extraction, and features analysis. After the definition of incipient fault based on the levels of fault, signal, and environmental nuisances, a paradigm is drawn between information-hiding domain and FDD. We will show that dissimilarity measure of probability density function (PDF) used for data hiding is efficient for incipient fault detection. The methodology is illustrated through incipient crack detection in a conductive material using eddy currents and short intermittent open-circuit duration in three-level neutralpoint-clamped inverter. The chapter also discusses fault detection threshold optimal setting and fault severity estimation.

Chapter Contents:

  • 3.1 Introduction
  • 3.2 Fault detection and diagnosis
  • 3.2.1 Methodology
  • 3.2.2 Application example of the methodology
  • 3.3 Incipient fault
  • 3.4 FDD as hidden information paradigm
  • 3.4.1 Introduction
  • 3.4.2 Distance measures
  • 3.4.3 Kullback–Leibler divergence
  • 3.5 Case studies
  • 3.5.1 Incipient crack detection
  • 3.5.2 Incipient fault in power converter
  • 3.5.3 Threshold setting
  • 3.5.4 Fault-level estimation
  • 3.6 Trends for KLD capability improvement
  • 3.7 Conclusion
  • References

Inspec keywords: mechanical engineering computing; feature extraction; probability; crack detection; fault diagnosis; data encapsulation

Other keywords: features analysis; information-hiding domain; FDD methods; incipient fault detection; probability density function; fault severity estimation; incipient crack detection; Kullback-leibler divergence; features extraction; incipient fault diagnosis

Subjects: Maintenance and reliability; Statistics; Data handling techniques; Mechanical engineering applications of IT; Civil and mechanical engineering computing; Other topics in statistics; Fracture mechanics and hardness (mechanical engineering); Mechanical components

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