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Updated: Jun 3, 2025

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A Novel Fault Diagnosis Method Using FCEEMD-Based Multi-Complexity Low-Dimensional Features and Directed Acyclic

Rongrong Lu1, Miao Xu1, Chengjiang Zhou1

  • 1School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China.

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|January 8, 2025
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Summary

This study introduces a novel fault diagnosis method for rotating machinery using Fast Complementary Ensemble Empirical Mode Decomposition (FCEEMD) and a directed acyclic graph LSTSVM. The approach achieves nearly 100% accuracy in identifying bearing and check valve faults.

Keywords:
directed acyclic graph least squares twin support vector machinefast complementary ensemble empirical mode decompositionfault diagnosisfault feature selectionfeature extraction

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Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Rolling bearings are vital for rotating machinery reliability.
  • Accurate fault diagnosis is essential for industrial safety and operational continuity.
  • Existing methods may struggle with complex signal noise and feature extraction.

Purpose of the Study:

  • To develop an advanced fault diagnosis method for rotating machinery.
  • To improve the accuracy and robustness of fault detection in critical industrial components.
  • To leverage advanced signal decomposition and machine learning techniques for enhanced diagnostics.

Main Methods:

  • Vibration signal decomposition using Fast Complementary Ensemble Empirical Mode Decomposition (FCEEMD) to reduce noise.
  • Extraction of nonlinear complexity features (SE, PE, DE, Gini coefficients) and traditional time/frequency domain features.
  • Low-dimensional feature selection using Robust Unsupervised Feature Selection with Local Preservation (RULSP).
  • Fault classification using a multi-classifier based on Directed Acyclic Graph LSTSVM (DAG LSTSVM).

Main Results:

  • The FCEEMD effectively reduced background noise in vibration signals.
  • A comprehensive set of features, including nonlinear complexity measures, captured signal characteristics.
  • RULSP successfully identified low-dimensional sensitive features from a high-dimensional matrix.
  • The DAG LSTSVM achieved nearly 100% diagnostic accuracy on laboratory bearing and industrial check valve faults.

Conclusions:

  • The proposed method demonstrates high effectiveness for fault diagnosis in rotating machinery.
  • The combination of FCEEMD, advanced feature extraction, and DAG LSTSVM offers superior diagnostic precision.
  • This approach has significant potential for enhancing industrial equipment reliability and predictive maintenance.