The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fau...The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fault characteristics under different loads is markedly inconsistent,and data is hard to label,which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different conditions.To address these issues,the paper proposes a novel transfer discriminant neural network(TDNN)for gear fault diagnosis.Specifically,an optimized joint distribution adaptive mechanism(OJDA)is designed to solve the distribution alignment problem between two domains.To improve the classification effect within the domain and the feature recognition capability for a few labeled data,metric learning is introduced to distinguish features from different fault categories.In addition,TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test result.The proposed TDNN is verified in the experimental data set of the artillery manipulator device,and the diagnosis can achieve 99.5%,significantly outperforming other traditional adaptation methods.展开更多
On the basis of practical projects in Chongqing,the thermal performance of heat exchangers (single U-tube type and double U-tube type) of the ground-source heat pump (GSHP) system in the hot summer was obtained and an...On the basis of practical projects in Chongqing,the thermal performance of heat exchangers (single U-tube type and double U-tube type) of the ground-source heat pump (GSHP) system in the hot summer was obtained and analyzed. The data obtained from test could match with the result deduced from theoretical calculation. From the test results,the cooling capacity of double U-tube is 1.6 times that of single U-tube. Taking cost per depth per watt Clq as the evaluation standard,Clq of single U-tube is 4.69 RMB$/W,and Clq of double U-tube is 3.14 RMB$/W. The double U-tube heat exchangers usage should be prioritized.展开更多
城市电网在发生N-1故障后,极可能新增运行风险,导致N-1-1时出现大面积停电事故。为管控城市电网N-1后运行风险,该文提出一种改进双智能体竞争双深度Q网络(dueling double deep Q network,D3QN)的城市电网N-1风险管控转供策略。根据风险...城市电网在发生N-1故障后,极可能新增运行风险,导致N-1-1时出现大面积停电事故。为管控城市电网N-1后运行风险,该文提出一种改进双智能体竞争双深度Q网络(dueling double deep Q network,D3QN)的城市电网N-1风险管控转供策略。根据风险管控原则,提出一种无需额外历史数据、考虑备自投装置、单供变电站风险和单供负荷母线风险的N-1场景指标;建立计及动作次序、指标间关系的负荷转供三阶段求解模型。以含预动作-变化探索值选择策略的改进双智能体D3QN方法,将负荷转供分为多个子转供环节学习,使转供思路清晰化,对动作空间进行降维,提高训练寻优效果,得到管控N-1风险的负荷转供策略。通过城市电网多场景算例分析,验证该文模型和方法的有效性。展开更多
文摘The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fault characteristics under different loads is markedly inconsistent,and data is hard to label,which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different conditions.To address these issues,the paper proposes a novel transfer discriminant neural network(TDNN)for gear fault diagnosis.Specifically,an optimized joint distribution adaptive mechanism(OJDA)is designed to solve the distribution alignment problem between two domains.To improve the classification effect within the domain and the feature recognition capability for a few labeled data,metric learning is introduced to distinguish features from different fault categories.In addition,TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test result.The proposed TDNN is verified in the experimental data set of the artillery manipulator device,and the diagnosis can achieve 99.5%,significantly outperforming other traditional adaptation methods.
文摘On the basis of practical projects in Chongqing,the thermal performance of heat exchangers (single U-tube type and double U-tube type) of the ground-source heat pump (GSHP) system in the hot summer was obtained and analyzed. The data obtained from test could match with the result deduced from theoretical calculation. From the test results,the cooling capacity of double U-tube is 1.6 times that of single U-tube. Taking cost per depth per watt Clq as the evaluation standard,Clq of single U-tube is 4.69 RMB$/W,and Clq of double U-tube is 3.14 RMB$/W. The double U-tube heat exchangers usage should be prioritized.
文摘城市电网在发生N-1故障后,极可能新增运行风险,导致N-1-1时出现大面积停电事故。为管控城市电网N-1后运行风险,该文提出一种改进双智能体竞争双深度Q网络(dueling double deep Q network,D3QN)的城市电网N-1风险管控转供策略。根据风险管控原则,提出一种无需额外历史数据、考虑备自投装置、单供变电站风险和单供负荷母线风险的N-1场景指标;建立计及动作次序、指标间关系的负荷转供三阶段求解模型。以含预动作-变化探索值选择策略的改进双智能体D3QN方法,将负荷转供分为多个子转供环节学习,使转供思路清晰化,对动作空间进行降维,提高训练寻优效果,得到管控N-1风险的负荷转供策略。通过城市电网多场景算例分析,验证该文模型和方法的有效性。
