Jun 2026· Journal of Physics, Conference Series· Vol 3272, pp. 012015· 0 citations· 8 references
Physics
TL;DR
The proposed IFGO-VMD-SVM (Improved fungal growth optimization algorithm - variational mode decomposition - support vector machine) intelligent diagnosis method achieves the highest diagnostic accuracy, verifying its effectiveness and superiority.
Abstract
To solve the problems of difficult feature extraction using the VMD method and low fault diagnosis accuracy in the process of bearing fault diagnosis, this paper proposes the IFGO-VMD-SVM (Improved fungal growth optimization algorithm - variational mode decomposition - support vector machine) intelligent diagnosis method. First, the IFGO algorithm is used to optimize the parameters of Variational Mode Decomposition (VMD) to achieve fault feature extraction. Then, it is combined with SVM for fault diagnosis. Compared with the VMD-SVM models based on different optimization algorithms, experiments show that the proposed model achieves the highest diagnostic accuracy, verifying its effectiveness and superiority.
Aiming at the difficulties in extracting gear fault feature information and the unsatisfactory performance of commonly used machine learning classification models in terms of feature separability and stable within-distribution recognition, this paper proposes a fault diagnosis method combining variational mode decompos...
Duo-Wen Chen, Hao-Ze Lv, Xin-Ze Li et al.· Entropy· 0 citations
A novel rolling bearing fault diagnosis approach integrating IZOA-VMD and the Teager envelope spectrum is proposed to overcome the inaccuracies of manual VMD parameter tuning. An Improved Zebra Optimization Algorithm (IZOA)— incorporating Sine chaotic mapping, elite opposition-based learning, and dynamic neighborhood l...
Compared with other algorithms, the presented method achieves superior identification performance and possesses favorable recognition capability for incipient weak faults, which can realize the classification of bearing faults.
In view of the issues of sample imbalance and low diagnostic accuracy associated with traditional methods for oil-immersed power transformer fault diagnosis, an ensemble support vector machine (SVM)-based fault diagnosis model is proposed, which incorporates the Tomek link removal (TLR)–weighted SMOTE (WSMOTE) oversamp...
Yan Zhao, Jun-Wei Wang, Bai-Yang Zhou· Energies· 0 citations
To overcome the problems such as the empirical parameters tuning, suboptimal feature selection, and poor classifier generalization in the existing bearing fault diagnosis methods, an NRBO - SVMD - KELM method is proposed in this paper. Newton-Raphson-based Optimizer (NRBO) adaptively optimizes the penalty factor of suc...
Li-Cheng Wang, Ji-Yang Sun, Yu-Sheng Zhang et al.· International Conference on...· 0 citations
To effectively extract fault features from motor bearings and improve the accuracy of fault diagnosis, this paper proposes a novel method for motor bearing fault diagnosis based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Golden Jackal Optimization (GJO) to optimize Convolutional...
Yu-Peng Li, Bo Yang, Ying Li· European Conference on Elect...· 0 citations
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