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Updated: Nov 27, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault

Yong Lv1,2, Yi Zhang1,2, Cancan Yi1,2

  • 1Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an improved adaptive local iterative filtering method for early fault detection in rolling bearings. The new technique enhances feature extraction from vibration signals, enabling more accurate diagnosis of bearing failures.

Keywords:
adaptive local iterative filteringfault diagnosisparticle swarm optimizationpermutation entropy

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Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Condition Monitoring

Background:

  • Early fault detection in rolling bearings is challenging due to weak fault signals and difficulties in feature extraction.
  • Traditional time-frequency analysis methods like adaptive local iterative filtering (ALIF) face issues such as modal aliasing and uncertain component numbers.
  • Accurate diagnosis of bearing health is crucial for preventing catastrophic failures and ensuring operational reliability.

Purpose of the Study:

  • To propose an improved adaptive local iterative filtering (ALIF) algorithm for enhanced fault diagnosis in rolling bearings.
  • To address the limitations of traditional ALIF, specifically the selection of threshold parameters and the number of components.
  • To develop a robust method for extracting early fault features from bearing vibration signals.

Main Methods:

  • An improved adaptive local iterative filtering (ALIF) algorithm is developed, integrating particle swarm optimization (PSO) and permutation entropy.
  • Particle swarm optimization (PSO) is employed to optimize threshold parameters and determine the number of components within ALIF.
  • Permutation entropy is utilized to evaluate and select the most informative mode components for fault feature analysis.

Main Results:

  • The proposed method effectively overcomes modal aliasing and component number uncertainty inherent in traditional decomposition techniques.
  • Numerical simulations and experimental data analysis demonstrate the superior performance of the improved ALIF algorithm in identifying bearing faults.
  • Enhanced feature extraction capabilities lead to more reliable early fault detection in rolling bearings.

Conclusions:

  • The enhanced adaptive local iterative filtering algorithm, optimized by PSO and permutation entropy, provides a powerful tool for rolling bearing fault diagnosis.
  • This approach significantly improves the accuracy and reliability of early fault feature extraction from vibration signals.
  • The method offers a promising solution for condition monitoring and predictive maintenance in mechanical systems.