Fault Feature Extraction Method of Gearbox based on Parameter Optimization VMD by Ding Chengjun, Fu Xiaoyang, Feng Yubo, Zhang Liang

“…In order to solve the problem that the signal-to-noise ratio of the gearbox fault signal is low and fault feature extraction is difficult，a method for extracting gearbox fault feature based on parameters optimized variational mode decomposition is proposed. …”
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Bearing fault diagnosis algorithm based on maximum correlated kurtosis feature mode decomposition and compound Gini index by YANG Gang, XU Wuyi, DENG Qin, QIN Limu, WEI Yuqian

“…The proposed method was verified for effectiveness by using analog and experimental signals, and comparative studies have shown that it is more effective in extracting periodic fault features compared with the parameter-optimized variational modal decomposition (VMD) and fixed-parameter MCKFMD.…”
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Transmission Tower Tilt State Recognition Based on Parameter Optimization of VMD-SVD and LSTM by Long ZHAO, Guanru WEN, Zhicheng LIU, Peng YUAN, Xinsheng DONG

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Research on axle-box bearing fault feature extraction algorithm based on simulation test and BOA-VMD by ZHANG Dongxing, YANG Gang, ZHOU Ao, QIN Limu, WEI Yuqian, YAN Lei

“…Aiming at the problem that axle-box bearing faults are difficult to find during the operation of urban rail trains, a bearing fault feature extraction based on variational mode decomposition (VMD) parameter optimization using butterfly optimization algorithm (BOA) was proposed. …”
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A hybrid framework: singular value decomposition and kernel ridge regression optimized using mathematical-based fine-tuning for enhancing river water level forecasting by Iman Ahmadianfar, Aitazaz Ahsan Farooque, Mumtaz Ali, Mehdi Jamei, Mozhdeh Jamei, Zaher Mundher Yaseen

“…Hence, a novel hybrid model is provided, incorporating singular value decomposition (SVD) in conjunction with kernel-based ridge regression (SKRidge), multivariate variational mode decomposition (MVMD), and the light gradient boosting machine (LGBM) as a feature selection method, along with the Runge–Kutta optimization (RUN) algorithm for parameter optimization. …”
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Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms by Lan-ting Zhou, Guan-lin Long, Can-can Hu, Kai Zhang

“…By integrating the adaptive complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and fuzzy entropy (FE) with the new and highly efficient Runge–Kuta optimizer (RUN), adaptive parameter optimization for the support vector machine (SVM) and radial basis function neural network (RBFNN) algorithms was achieved. …”
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