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Signal-processing techniques utilized in fault diagnosis procedures

Signal-processing techniques utilized in fault diagnosis procedures

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Signal processing is a widely used technology that pervades every aspect of our lives which are actually unknown to many. This chapter surveys some basic concepts which drive the field and focuses on the other two types of analyses, i.e., the frequency- and time-frequency analyses. First, the mathematical concepts are explained; then MATLAB implementation of transforms, along with intuitive examples, is provided in order to address the corresponding technical issues during the implementation and postprocessing analysis.

Chapter Contents:

  • 7.1 Introduction
  • 7.2 Fourier transform
  • 7.2.1 Recursive FFT algorithm in MATLAB
  • 7.2.2 Iterative FFT algorithm in MATLAB
  • 7.2.3 Example
  • 7.3 Short-time Fourier transform
  • 7.4 Multiresolution analysis
  • 7.5 Wavelet transform
  • 7.5.1 Resolution in time-frequency map
  • 7.6 Inverse wavelet transform
  • 7.7 Discrete of wavelet transform
  • 7.8 Hilbert-Huang transform
  • References

Inspec keywords: time-frequency analysis; signal processing; fault diagnosis

Other keywords: fault diagnosis procedures; signal processing techniques; time-frequency analyses

Subjects: Signal processing theory; Signal processing and detection; Digital signal processing; Mathematical analysis; Mathematical analysis

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