Weak fault diagnosis method for rotorcraft bearings based on whale optimization algorithm—Optimized simplistic geometry mode decomposition and maximum correlated kurtosis deconvolution

Liang Gao; Changhong Li

doi:10.59400/adecp2818

Weak fault diagnosis method for rotorcraft bearings based on whale optimization algorithm—Optimized simplistic geometry mode decomposition and maximum correlated kurtosis deconvolution

Liang Gao School of electronic information engineering, Xi’an Technological University, Shaanxi 710021, China
Changhong Li School of electronic information engineering, Xi’an Technological University, Shaanxi 710021, China

Article ID: 2818

DOI: https://doi.org/10.59400/adecp2818

Keywords: rolling bearing; SGMD; MCKD; WOA

Abstract

Early fault signals of the rolling bearing in the rotor are weak and present the characteristics of non-periodic and non-stationary; it is more difficult to carry out fault diagnosis on it. In this regard, this paper proposes a weak rolling bearing fault diagnosis algorithm based on whale optimization algorithm, simplistic geometry mode decomposition, and maximum correlated kurtosis deconvolution (WOA-SGMD-MCKD). Firstly, the vibration signal of the rotor platform is obtained, and the Symmetric Geometric Mode Decomposition (SGMD) is used to reconstruct the vibration signal. To obtain the best decomposition effect of the SGMD and overcome modal aliasing, the Whale Optimization Algorithm (WOA) is used to optimize the embedding dimension. Secondly, for the reconstructed vibration signal, the Maximum Correlated Kurtosis Deconvolution (MCKD) is used to extract its impulse component, and the WOA is used to optimize the filter length and deconvolution period of the MCKD so that the frequency envelope spectrum of the vibration signal can be obtained, which can provide the basis for the fault diagnosis of rolling bearings. Finally, the effectiveness and feasibility of the algorithm proposed are verified by a non-periodic and non-stationary simulation platform and rotor maneuvering platform in this paper.

Published

2025-05-12

How to Cite

Gao, L., & Li, C. (2025). Weak fault diagnosis method for rotorcraft bearings based on whale optimization algorithm—Optimized simplistic geometry mode decomposition and maximum correlated kurtosis deconvolution. Advances in Differential Equations and Control Processes, 32(2), 2818. https://doi.org/10.59400/adecp2818

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Issue

Vol. 32 No. 2 (2025)

Section

Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

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