目的针对深潜耐压球壳在真实下潜过程中全局应力场难以直接获取的问题,提出一种基于人工智能的深潜耐压球壳应力场映射算法。方法构建深潜耐压球壳有限元模型,并开展仿真分析。提出深潜耐压球壳监测布点方案,进而利用长短时记忆神经网络...目的针对深潜耐压球壳在真实下潜过程中全局应力场难以直接获取的问题,提出一种基于人工智能的深潜耐压球壳应力场映射算法。方法构建深潜耐压球壳有限元模型,并开展仿真分析。提出深潜耐压球壳监测布点方案,进而利用长短时记忆神经网络(Long-short Term Memory Network,LSTM),将测点应力信息作为输入,将全局应力场信息作为输出,构建深潜耐压球壳应力场映射模型。最后,对不同测点下的映射结果进行分析。结果与模型试验结果相比,仿真误差小于2%。与DNN模型及BP模型相比,映射误差分别下降94.92%与97.76%。结论所提映射算法可在部分测点失效的情况下仍可以保持较高精度。展开更多
In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(...In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.展开更多
The effect of static transmission error on nonlinear dynamic response of the spiral bevel gear system combining with time-varying stiffness and backlash was investigated.Firstly,two different control equations of the ...The effect of static transmission error on nonlinear dynamic response of the spiral bevel gear system combining with time-varying stiffness and backlash was investigated.Firstly,two different control equations of the spiral bevel gear model were adopted,where the static transmission error was expressed in two patterns as predesigned parabolic function and sine function of transmission errors.The dynamic response,bifurcation map,time domain response,phase curve and Poincare map were obtained by applying the explicit Runge-Kutta integration routine with variable-step.A comparative study was carried out and some profound phenomena were detected.The results show that there are many different kinds of tooth rattling phenomena at low speed.With the increase of speed,the system enters into stable motion without any rattling in the region(0.72,1.64),which indicates that the system with predesigned parabolic function of transmission error has preferable capability at high speed.展开更多
文摘目的针对深潜耐压球壳在真实下潜过程中全局应力场难以直接获取的问题,提出一种基于人工智能的深潜耐压球壳应力场映射算法。方法构建深潜耐压球壳有限元模型,并开展仿真分析。提出深潜耐压球壳监测布点方案,进而利用长短时记忆神经网络(Long-short Term Memory Network,LSTM),将测点应力信息作为输入,将全局应力场信息作为输出,构建深潜耐压球壳应力场映射模型。最后,对不同测点下的映射结果进行分析。结果与模型试验结果相比,仿真误差小于2%。与DNN模型及BP模型相比,映射误差分别下降94.92%与97.76%。结论所提映射算法可在部分测点失效的情况下仍可以保持较高精度。
基金Project(50977003) supported by the National Natural Science Foundation of China
文摘In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.
基金Project(2011CB706800) supported by the National Basic Research Program of ChinaProject(51275530) supported by the National Natural Science Foundation of China
文摘The effect of static transmission error on nonlinear dynamic response of the spiral bevel gear system combining with time-varying stiffness and backlash was investigated.Firstly,two different control equations of the spiral bevel gear model were adopted,where the static transmission error was expressed in two patterns as predesigned parabolic function and sine function of transmission errors.The dynamic response,bifurcation map,time domain response,phase curve and Poincare map were obtained by applying the explicit Runge-Kutta integration routine with variable-step.A comparative study was carried out and some profound phenomena were detected.The results show that there are many different kinds of tooth rattling phenomena at low speed.With the increase of speed,the system enters into stable motion without any rattling in the region(0.72,1.64),which indicates that the system with predesigned parabolic function of transmission error has preferable capability at high speed.
