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## Kullback–Leibler divergence for incipient fault diagnosis

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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 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

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