Search Results (2)

Aiming at the noise and nonlinear characteristics existing in the deformation monitoring data of concrete dams, this paper proposes a dam deformation prediction model based on a multi-scale adaptive kernel ensemble. The model incorporates Gaussian white noise as a random factor and uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to decompose the data set finely. Each modal component is evaluated by sample entropy (SE) analysis so that the data set can be reconstructed according to the sample entropy value to retain key information. In addition, the model uses partial autocorrelation function (PACF) to determine the correlation between intrinsic modal function (IMF) and historical data. Then, the global search whale optimization algorithm (GSWOA) is used to accurately determine the parameters of kernel extreme learning machine (KELM), which forms the basis of the dam deformation prediction model based on multi-scale adaptive kernel function. The case analysis shows that CEEMDAN-SE-PACF can effectively extract signal features and identify significant components and trends so as to better understand the internal deformation trend of the dam. In terms of algorithm optimization, compared with the WOA algorithm and other algorithms, the results of the GSWOA algorithm are significantly better than other algorithms and have the optimal convergence. In terms of prediction performance, CEEMDAN-SE-PACF-GSWOA-KELM is superior to the CEEMDAN-WOA-KELM, GSWOA-KELM, CEEMDAN-KELM, and KELM models, showing higher accuracy and stronger stability. This improvement is manifested in the decrease of root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) and the improvement of the R square (R²) value close to 1. These research results provide a new method for dam safety monitoring and evaluation. Full article

To improve the accuracy of gear fault diagnosis and overcome the low diagnostic accuracy of the model caused by manual parameter selection, a combined diagnostic model based on time-frequency fusion features is combined with the improved global search whale optimization algorithm (GSWOA) to optimize the fault diagnosis capability of the kernel extreme learning machine (KELM). First, the time-domain and frequency-domain features of the gear fault state are extracted separately, and feature vectors are constructed through feature fusion, which overcomes the limitations of single features. Second, the GSWOA based on three strategies is used to optimize the regularization coefficient C and kernel function parameter γ of KELM, and a GSWOA-KELM fault diagnosis model is built to avoid the problem of low fault diagnosis accuracy caused by the manual selection of KELM parameters. Finally, the public dataset from Southeast University is taken to verify the performance of the proposed model by comparing it with KELM, SSA-KELM, and WOA-KELM models. The experimental results demonstrate that the improved time-frequency fusion features-based GSWOA-KELM model shows faster convergence speed and stronger global search ability. Compared with KELM, SSA-KELM, and WOA-KELM models, the performance of the proposed model has been improved by 11.33%, 8.67%, and 1.33%, respectively. Full article