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A novel mathematical morphology spectrum entropy based on scale-adaptive techniques.

Rui Yao1, Chen Guo1, Wu Deng2

  • 1College of Marine Electrical Engineering, Dalian Maritime University of China, Dalian 116026, China.

ISA Transactions
|August 27, 2021
PubMed
Summary

A novel scale-adaptive mathematical morphology spectrum entropy (AMMSE) method enhances signal feature extraction for bearing fault diagnosis. AMMSE automatically selects optimal scale parameters, improving accuracy in performance degradation and fault degree quantification.

Keywords:
Adaptive scaleFault degree identificationFeature extractionMathematical morphology spectrum entropyMathematical morphology spectrum propertiesPerformance degradation evaluation

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Area of Science:

  • Signal Processing
  • Mechanical Engineering
  • Information Theory

Background:

  • Mathematical morphology spectrum entropy (MMSE) is a signal feature extraction technique.
  • The scale of the structure element is a critical parameter affecting MMSE accuracy.
  • Existing scale selection methods are often fixed and unsuitable for quantifying bearing performance degradation or fault severity.

Purpose of the Study:

  • To propose a scale-adaptive mathematical morphology spectrum entropy (AMMSE) method for improved scale selection.
  • To address limitations of fixed scale selection in existing methods.
  • To develop a method adaptable to signal characteristics for bearing fault analysis.

Main Methods:

  • Proved non-negativity and monotonic decreasing properties of the mathematical morphology spectrum (MMS).
  • Developed two adaptive scale selection strategies based on MMS properties to minimize feature loss.
  • Integrated strategies into the proposed AMMSE method.

Main Results:

  • The proposed AMMSE method is not constrained by experimental parameters or external signal information.
  • AMMSE scale adapts to signal characteristics, offering greater generalizability.
  • AMMSE demonstrated superior performance in identifying fault degrees on the CWRU bearing dataset and evaluating performance degradation on the IMS bearing dataset.

Conclusions:

  • AMMSE offers a robust and adaptive approach to scale selection in MMSE.
  • The method effectively quantifies bearing performance degradation and fault degrees.
  • AMMSE provides a more generalizable and accurate feature extraction tool for mechanical fault diagnosis.