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Research on Fault Diagnosis Method with Adaptive GTO Optimized VMD and SVM Parameters
Version 1
: Received: 8 August 2024 / Approved: 9 August 2024 / Online: 9 August 2024 (09:02:11 CEST)
How to cite: Fang, T.; Ma, L.; Zhang, H. Research on Fault Diagnosis Method with Adaptive GTO Optimized VMD and SVM Parameters. Preprints 2024, 2024080675. https://doi.org/10.20944/preprints202408.0675.v1 Fang, T.; Ma, L.; Zhang, H. Research on Fault Diagnosis Method with Adaptive GTO Optimized VMD and SVM Parameters. Preprints 2024, 2024080675. https://doi.org/10.20944/preprints202408.0675.v1
Abstract
To address the issue of intelligent optimization algorithms being prone to local optima, resulting in insufficient feature extraction and low fault type recognition rates when optimizing Variational Mode Decomposition (VMD) and Support Vector Machine (SVM) parameters, this paper proposes a fault diagnosis method based on an improved Artificial Gorilla Troops Optimization (GTO) algorithm. The GTO algorithm is enhanced using Logistic chaotic mapping, a linear decreasing weight factor, the global exploration strategy of the Osprey Optimization Algorithm, and the Levy flight strategy, improving its ability to escape local optima, adaptability, and convergence accuracy. This algorithm is used to optimize the parameters of VMD and SVM for fault diagnosis. Experiments on fault diagnosis with two datasets of different sample sizes show that the proposed method achieves a diagnostic accuracy of no less than 98% for samples of varying sizes, with stable and reliable results.
Keywords
variational mode decomposition; support vector machine; artificial gorilla troops optimization; fault diagnosis
Subject
Engineering, Mechanical Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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