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Settlement monitoring system of pile-group foundation
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作者 陈志坚 张宁宁 张雄文 《Journal of Central South University》 SCIE EI CAS 2011年第6期2122-2130,共9页
In order to realize information construction on settlement of pile-group foundation of Sutong Bridge, the monitoring instruments of high-precision micro-pressure sensor and hydrostatic leveling and settlement profiler... In order to realize information construction on settlement of pile-group foundation of Sutong Bridge, the monitoring instruments of high-precision micro-pressure sensor and hydrostatic leveling and settlement profiler were integrated synthetically. A set of practical multi-scale monitoring system on settlement of super-large pile-group foundation in deep water was put forward. The reliable settlement results are obtained by means of multi-sensor data fusion. Finite element model of pile-group foundation is established. By analysis of finite element simulated calculation of pile-group foundation, rules of settlement and uneven settlement obtained by monitoring and calculation results are coincident and the absolute error of settlement between them is 4.7 mm. The research shows that it is reasonable and feasible to monitor settlement of pile-group foundation with the system, and it can provide a method for the same type pile-group foundation in deep water. 展开更多
关键词 pile-group foundation SETTLEMENT monitoring system multi-sensor data fusion
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基于嵌套长短期记忆网络的机械装备剩余使用寿命预测方法 被引量:5
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作者 程一伟 朱海平 +1 位作者 吴军 邵新宇 《中国科学:技术科学》 EI CSCD 北大核心 2022年第1期76-87,共12页
剩余使用寿命(remaining useful life,RUL)预测是保障机械装备可靠性、可用性和安全性的重要技术.本文提出一种基于嵌套长短期记忆(nested long short-term memory,NLSTM)网络的机械装备RUL预测方法,它通过融合多传感器监测信号,实现对... 剩余使用寿命(remaining useful life,RUL)预测是保障机械装备可靠性、可用性和安全性的重要技术.本文提出一种基于嵌套长短期记忆(nested long short-term memory,NLSTM)网络的机械装备RUL预测方法,它通过融合多传感器监测信号,实现对机械装备RUL的精确预测.区别于普通LSTM网络,NLSTM将存储单元进一步加深,将一个LSTM神经元结构嵌套在原有LSTM的存储空间中,实现对多传感器时间序列信号中长期依赖性的深度捕捉.本文使用涡扇发动机和加工刀具两个实验案例来验证NLSTM的预测性能;从涡扇发动机案例验证可知,相比于LSTM,NLSTM的预测性能在两个指标上分别整体提升了4.66%和15.18%,且NLSTM的预测结果也优于文献中的其他先进方法;从加工刀具案例验证可知,NLSTM的预测结果在六个刀具上的预测结果均优于LSTM. 展开更多
关键词 循环神经网络 嵌套长短期记忆网络 剩余使用寿命预测 多传感监测数据 机械装备
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