School of Optoelectronic Engineering and Instrumentation Science, Dalian University of Technology, Dalian 116024, China.
Bridge and Tunnel Research and Development Center, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel). 2020 Dec 23;21(1):37. doi: 10.3390/s21010037.
Low-coherent fiber-optic sensors combined with neural network algorithms were designed to carry out a load-sensitizing spherical bearing. Four sensing fibers were wound around the outside of the pot support of the spherical bearing uniformly deployed from upper to bottom. The upper three were configured in a distributed way to respond to the applied load as a function of the three strain sensors. The bottom one was employed as a temperature compensation sensor. A loading experiment was implemented to test the performance of the designed system. The results showed that there was a hysteresis in all the three sensors between loading and unloading process. The neural network algorithm is proposed to set up a function of the three sensors, treated as a set of input vectors to establish the input-output relationship between the applied loads and the constructed input vectors, in order to overcome the hysteresis existing in each sensor. An accuracy of 6% for load sensing was approached after temperature compensation.
低相干光纤传感器与神经网络算法相结合，设计了一种用于进行负载敏感的球轴承。四个传感光纤均匀地缠绕在球轴承的锅支撑外部，从上部到底部均匀部署。上面的三个以分布式方式配置，以响应三个应变传感器作为函数的施加负载。底部一个用作温度补偿传感器。进行了加载实验来测试设计系统的性能。结果表明，在加载和卸载过程中，所有三个传感器都存在滞后。提出了神经网络算法来建立三个传感器的函数，将其视为一组输入向量，以建立施加负载与构建输入向量之间的输入-输出关系，以克服每个传感器中的滞后。经过温度补偿后，接近了 6%的负载传感精度。
