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Remaining useful life prediction for bearings based on Transformer-BiLSTM network optimized by the Newton-Raphson-based optimizer 认领 引用
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作者 Wen Juan Wu You +1 位作者 Song Yang Pan Baisong 《High Technology Letters》 EI CAS 2026年第2期109-120,共12页
The rolling bearing is one of the critical components in mechanical equipment,and predicting its remaining useful life(RUL)is of great significance in enterprise production processes.While deep learning-based approach... The rolling bearing is one of the critical components in mechanical equipment,and predicting its remaining useful life(RUL)is of great significance in enterprise production processes.While deep learning-based approaches have achieved great success for bearing prognosis,most of them are not capable of mining both global and local information from the run-to-failure data.In addition,hyperparameters such as the number of hidden layer neurons,learning rate,and regularization parameters in neural networks still rely heavily on manual experience for setting.To address these issues,a novel framework for predicting the RUL of bearings based on the Transformer and the bidirectional long short-term memory(Transformer-BiLSTM)is proposed,and the Newton-Raphsonbased optimizer(NRBO)is introduced to determine the crucial parameters of the network.Firstly,degradation sensitive features are extracted and selected from the raw vibration signals,forming the input for the prediction model.Secondly,the mean absolute error(MAE)between the predicted and actual values is utilized as the fitness function of the NRBO algorithm to optimize the Transformer-BiLSTM model,searching for the optimal values of the key hyperparameters.Finally,the optimized model is used for RUL prediction,and its performance is validated on publicly available datasets.The results demonstrate that the proposed method can achieve the optimal hyperparameter combination without relying on empirical guidance.Compared with the unoptimized model,the optimized prediction model reduces the MAE and root mean squared error(RMSE)by 6.50%and 9.91%,respectively. 展开更多
关键词 Transformer bidirectional long short-term memory Newton-Raphson-based optimizer remaining useful life prediction bearing
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Prediction of mixed-modeⅠ/Ⅱfracture toughness of rock-concrete bi-material disc with interface crack:Interpretable NRBO-XGBoost-SHAP model and experimental validation 认领 引用
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作者 Tengfei Guo Congxiang Yuan +3 位作者 Xu Chang Zhijun Zhang Guicheng He Yichao Rui 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第7期1453-1473,共21页
Accurately determining the effective fracture toughness(Keff)of rock-concrete(R-C)bi-materials,governed by interface inclination and ambient temperature,is a prerequisite for assessing their structural stability.Th... Accurately determining the effective fracture toughness(Keff)of rock-concrete(R-C)bi-materials,governed by interface inclination and ambient temperature,is a prerequisite for assessing their structural stability.This study developed a hybrid NRBO-XGBoost prediction model using the Newton-RaphsonBased Optimizer(NRBO)to tune the hyperparameters of Extreme Gradient Boosting(XGBoost)model.The established model was developed based on 154 datasets obtained from laboratory tests and numerical simulations with the cracked straight-through Brazilian disc(CSTBD)specimens,including twelve input parameters.The NRBO-XGBoost model for Keffprediction was investigated and compared with seven more models.Furthermore,the Shapley Additive exPlanations(SHAP)method was employed to quantify the contributions of inputs to Keffto improve the interpretability of the developed model.Finally,new data were used to validate the model.Evaluation results demonstrate that metaheuristic optimization algorithms significantly enhance the performance of XGBoost,with NRBO-XGBoost performing the best.The models rank from highest to lowest prediction performance as follows:NRBO-XGBoost,WOA-XGBoost,PSO-XGBoost,XGBoost,RF,CatBoost,LightGBM,and AdaBoost.The interpretable analysis shows that the interface inclination angle exerts the dominant influence.The validation results demonstrate that NRBO-XGBoost achieves high predictive accuracy on a new dataset,showing promising implications for practical applications. 展开更多
关键词 Effective fracture toughness R-C bi-material Extreme Gradient Boosting Newton-Raphson-Based Optimizer SHAP
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