As the critical equipment,large axial-flow fan(LAF)is used widely in highway tunnels for ventilating.Note that any malfunction of LAF can cause severe consequences for traffic.Specifically,fault deterioration is suppr...As the critical equipment,large axial-flow fan(LAF)is used widely in highway tunnels for ventilating.Note that any malfunction of LAF can cause severe consequences for traffic.Specifically,fault deterioration is suppressed tremendously when an abnormal state is detected in the stage of early fault.Thus,the monitoring of the early fault characteristics is very difficult because of the low signal amplitude and system disturbance(or noise).In order to overcome this problem,a novel early fault judgment method to predict the operation trend is proposed in this paper.The vibration-electric information fusion,the support vector machine(SVM)with particle swarm optimization(PSO),and the cross-validation(CV)for predicting LAF operation states are proposed and discussed.Finally,the results of the experimental study verify that the performance of the proposed method is superior to that of the contrast models.展开更多
A two-dimensioual stress analysis was developed to evaluate the failure of composite joints using characteristic length method. In this study, the accuracy of characteristic length method on the prediction of failure ...A two-dimensioual stress analysis was developed to evaluate the failure of composite joints using characteristic length method. In this study, the accuracy of characteristic length method on the prediction of failure strength and failure mode using different failure criteria was investigated. The stresses required for evaluating the joints were computed from stress functions obtained from displacement expressions that satisfy boundary conditions of the hole. The available experimental data for joint strength in literature were compared with the predicted failure loads and modes of failure for different composite pinned joints. No single failure criterion utilized to evaluate the failure gave a universally best fit across the three joints evaluated. However, the accuracy of characterizing the joints failure varies with joint laminate and choice of failure criterions.展开更多
基金Project(2018YFB2002100)supported by the National Key R&D Program of China。
文摘As the critical equipment,large axial-flow fan(LAF)is used widely in highway tunnels for ventilating.Note that any malfunction of LAF can cause severe consequences for traffic.Specifically,fault deterioration is suppressed tremendously when an abnormal state is detected in the stage of early fault.Thus,the monitoring of the early fault characteristics is very difficult because of the low signal amplitude and system disturbance(or noise).In order to overcome this problem,a novel early fault judgment method to predict the operation trend is proposed in this paper.The vibration-electric information fusion,the support vector machine(SVM)with particle swarm optimization(PSO),and the cross-validation(CV)for predicting LAF operation states are proposed and discussed.Finally,the results of the experimental study verify that the performance of the proposed method is superior to that of the contrast models.
文摘A two-dimensioual stress analysis was developed to evaluate the failure of composite joints using characteristic length method. In this study, the accuracy of characteristic length method on the prediction of failure strength and failure mode using different failure criteria was investigated. The stresses required for evaluating the joints were computed from stress functions obtained from displacement expressions that satisfy boundary conditions of the hole. The available experimental data for joint strength in literature were compared with the predicted failure loads and modes of failure for different composite pinned joints. No single failure criterion utilized to evaluate the failure gave a universally best fit across the three joints evaluated. However, the accuracy of characterizing the joints failure varies with joint laminate and choice of failure criterions.
