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A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology.

Huimin Zhao1,2,3,4, Shaoyan Zuo5, Ming Hou6

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Summary
This summary is machine-generated.

A new method, the Maximum-Minimum Length Curve Enhanced Empirical Wavelet Transform (MSCEWT), improves motor bearing fault diagnosis by effectively decomposing vibration signals. This approach overcomes limitations of traditional methods, enabling accurate identification of bearing faults.

Keywords:
empirical wavelet transformfeature extractionmaximum-minimum length curvescale space transformationspectrum segmentation

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Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Condition Monitoring

Background:

  • Empirical Wavelet Transform (EWT) faces limitations in Fourier segmentation due to reliance on spectral amplitude maxima.
  • Accurate fault diagnosis in motor bearings is crucial for industrial machinery maintenance.

Purpose of the Study:

  • To propose and validate an enhanced EWT (MSCEWT) for improved motor bearing fault diagnosis.
  • To address the shortcomings of EWT's Fourier segmentation in signal decomposition.

Main Methods:

  • Developed the Maximum-Minimum Length Curve method to transform vibration signal spectra into scale space.
  • Applied MSCEWT to decompose vibration signals into Intrinsic Mode Functions (IMFs).
  • Utilized Hilbert transform and power spectrum analysis for fault feature frequency extraction and comparison.

Main Results:

  • MSCEWT effectively decomposes bearing vibration signals, yielding fewer IMFs than EMD and EEMD.
  • The method accurately extracts motor bearing fault feature frequencies.
  • MSCEWT demonstrates superior performance compared to EMD and EEMD in fault diagnosis.

Conclusions:

  • The Maximum-Minimum Length Curve method enhances EWT, overcoming Fourier segmentation limitations.
  • MSCEWT provides an effective and novel approach for motor bearing fault diagnosis.
  • This study offers a new technique for rotating machinery fault diagnosis.