What question did this study set out to answer?

This research aims to enhance the fault diagnosis of rolling bearings using an improved optimization algorithm.

January 6, 2026Mathematics0 citationsOpen Access

Research on Rolling Bearing Fault Diagnosis Based on IRBMO-CYCBDf

DGDawei GuoNorthwestern Polytechnical University JCJiaxun ChenDalian Jiaotong University XLXiaodong LiuDalian Jiaotong University

Key Points

This research aims to enhance the fault diagnosis of rolling bearings using an improved optimization algorithm.
Introduced Improved Red-Billed Blue Magpie Optimizer to optimize parameters for Cyclostationary Blind Deconvolution.
Utilized fitness function based on Improved Envelope Spectrum Entropy for parameter optimization.
Applied support vector machine for multi-fault classification based on optimized parameters.
Conducted simulation tests with vibration signals under various noise conditions.
Achieved maximum diagnostic accuracy of 100% for fault diagnosis in metro train bogies.
Significantly improved signal-to-noise ratio of processed signals from rolling bearings.
Successfully identified shaft frequency components and their harmonics during analysis.

Abstract

This paper introduces an Improved Red-Billed Blue Magpie Optimizer (IRBMO) to enhance the Maximum Second-Order Cyclostationary Blind Deconvolution (CYCBD) method, which traditionally depends on manual, experience-based setting of its key parameters (filter length L and cyclic frequency α). By adopting an Improved Envelope Spectrum Entropy (EK) as the fitness function, the IRBMO autonomously optimizes these parameters, eliminating the need for prior knowledge and improving its applicability in industrial settings. The Improved Red-Billed Blue Magpie algorithm is employed to adaptively optimize the penalty parameter and kernel function parameter of the support vector machine, thereby obtaining an optimal support vector machine model. By introducing fuzzy entropy theory, the feature vectors of filtered signals—processed by the Cyclostationary Blind Deconvolution method with optimal parameters—are extracted and used as input for the optimally parameterized support vector machine, achieving multi-fault classification for bogie bearings. The results show that the IRBMO-CYCBD method significantly enhances the periodic weak fault impulse components and improves the signal-to-noise ratio of the processed signal. Envelope spectrum analysis of the filtered signal allows for clear observation of shaft frequency components, as evidenced by the accurate identification of the 110 Hz fundamental frequency and its harmonic components at 220 Hz, 330 Hz, and 440 Hz in the spectrum. Simulation tests verify the efficacy of the IRBMO-CYCBD method in processing rolling bearing vibration signals under strong noise interference. Under laboratory conditions, simulation experiments were conducted by collecting vibration acceleration signals from rolling bearings in various states. The aforementioned method was applied for fault diagnosis, achieving a maximum diagnostic accuracy of 100%. Through repeated experiments, it was verified that this method meets the fault diagnosis requirements for rolling bearings in metro train bogies.

问 AI

Bookmark

View Full Paper