In this paper,two lifting mechanism models with opposing placements,which use the same hydraulic hoist model and have the same angle of 50°,have been developed.The mechanical and hydraulic simulation models are e...In this paper,two lifting mechanism models with opposing placements,which use the same hydraulic hoist model and have the same angle of 50°,have been developed.The mechanical and hydraulic simulation models are established using MATLAB Simscape to analyze their kinetics and dynamics in the lifting and holding stages.The simulation findings are compared to the analytical calculation results in the steady state,and both methods show good agreement.In the early lifting stage,Model 1 produces greater force and discharges goods in the container faster than Model 2.Meanwhile,Model 2 reaches a higher force and ejects goods from the container cleaner than its counterpart at the end lifting stage.The established simulation models can consider the effects of dynamic loads due to inertial moments and forces generated during the system operation.It is crucial in studying,designing,and optimizing the structure of hydraulic-mechanical systems.展开更多
The conventional admittance approach utilizing statistical evaluation metrics offers limited information about the damage location,especially when damage introduces nonlinearities in admittance features.This study pro...The conventional admittance approach utilizing statistical evaluation metrics offers limited information about the damage location,especially when damage introduces nonlinearities in admittance features.This study proposes a novel automated damage localization method for plate-like structures based on deep learning of raw admittance signals.A one-dimensional(1D)convolutional neural network(CNN)-based model is designed to automate processing of raw admittance response and prediction of damage probabilities across multiple locations in a monitored structure.Raw admittance data set is augmented with white noise to simulate realistic measurement conditions.Stratified K-fold cross-validation technique is employed for training and testing the network.The experimental validation of the proposed method shows that the proposed method can accurately identify the state and damage location in the plate with an average accuracy of 98%.Comparing with established 1D CNN models reveals superior performance of the proposed method,with significantly lower testing error.The proposed method exhibits the ability to directly handle raw electromechanical admittance responses and extract optimal features,overcoming limitations associated with traditional piezoelectric admittance approaches.By eliminating the need for signal preprocessing,this method holds promise for real-time damage monitoring of plate structures.展开更多
基金Ho Chi Minh City University of Technology(HCMUT)Vietnam National University Ho Chi Minh City(VNU-HCM)for supporting this study。
文摘In this paper,two lifting mechanism models with opposing placements,which use the same hydraulic hoist model and have the same angle of 50°,have been developed.The mechanical and hydraulic simulation models are established using MATLAB Simscape to analyze their kinetics and dynamics in the lifting and holding stages.The simulation findings are compared to the analytical calculation results in the steady state,and both methods show good agreement.In the early lifting stage,Model 1 produces greater force and discharges goods in the container faster than Model 2.Meanwhile,Model 2 reaches a higher force and ejects goods from the container cleaner than its counterpart at the end lifting stage.The established simulation models can consider the effects of dynamic loads due to inertial moments and forces generated during the system operation.It is crucial in studying,designing,and optimizing the structure of hydraulic-mechanical systems.
文摘The conventional admittance approach utilizing statistical evaluation metrics offers limited information about the damage location,especially when damage introduces nonlinearities in admittance features.This study proposes a novel automated damage localization method for plate-like structures based on deep learning of raw admittance signals.A one-dimensional(1D)convolutional neural network(CNN)-based model is designed to automate processing of raw admittance response and prediction of damage probabilities across multiple locations in a monitored structure.Raw admittance data set is augmented with white noise to simulate realistic measurement conditions.Stratified K-fold cross-validation technique is employed for training and testing the network.The experimental validation of the proposed method shows that the proposed method can accurately identify the state and damage location in the plate with an average accuracy of 98%.Comparing with established 1D CNN models reveals superior performance of the proposed method,with significantly lower testing error.The proposed method exhibits the ability to directly handle raw electromechanical admittance responses and extract optimal features,overcoming limitations associated with traditional piezoelectric admittance approaches.By eliminating the need for signal preprocessing,this method holds promise for real-time damage monitoring of plate structures.
