An Integrated Mechanical Fault Diagnosis Framework Using Improved GOOSE-VMD, RobustICA, and CYCBD

An Integrated Mechanical Fault Diagnosis Framework Using Improved GOOSE-VMD, RobustICA, and CYCBD

Rolling element bearings serve as critical transmission components in industrial automation systems, yet their fault signatures are susceptible to interference from strong background noise, complex operating conditions, and nonlinear impact characteristics. Addressing the limitations of conventional...

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Bibliographic Details
Main Authors: Jingzong Yang, Xuefeng Li
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Machines
Subjects:
variational mode decomposition (VMD)
GOOSE
robust independent component analysis (RobustICA)
maximum second-order cyclostationary blind convolution (CYCBD)
fault diagnosis
Online Access:https://www.mdpi.com/2075-1702/13/7/631
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Summary:Rolling element bearings serve as critical transmission components in industrial automation systems, yet their fault signatures are susceptible to interference from strong background noise, complex operating conditions, and nonlinear impact characteristics. Addressing the limitations of conventional methods in adaptive parameter optimization and weak feature enhancement, this paper proposes an innovative diagnostic framework integrating Improved Goose optimized Variational Mode Decomposition (IGOOSE-VMD), RobustICA, and CYCBD. First, to mitigate modal aliasing issues caused by empirical parameter dependency in VMD, we fuse a refraction-guided reverse learning mechanism with a dynamic mutation strategy to develop the IGOOSE. By employing an energy-feature-driven fitness function, this approach achieves synergistic optimization of the mode number and penalty factor. Subsequently, a multi-channel observation model is constructed based on optimal component selection. Noise interference is suppressed through the robust separation capabilities of RobustICA, while CYCBD introduces cyclostationarity-based prior constraints to formulate a blind deconvolution operator with periodic impact enhancement properties. This significantly improves the temporal sparsity of fault-induced impact components. Experimental results demonstrate that, compared to traditional time–frequency analysis techniques (e.g., EMD, EEMD, LMD, ITD) and deconvolution methods (including MCKD, MED, OMEDA), the proposed approach exhibits superior noise immunity and higher fault feature extraction accuracy under high background noise conditions.
ISSN:2075-1702

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