Department of Civil Engineering, Zhejiang University City College, Hangzhou 310015, China.
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Sensors (Basel). 2018 Jul 6;18(7):2176. doi: 10.3390/s18072176.
Techniques based on the elasto-magnetic (EM) effect have been receiving increasing attention for their significant advantages in cable stress/force monitoring of in-service structures. Variations in ambient temperature affect the magnetic behaviors of steel components, causing errors in the sensor and measurement system results. Therefore, temperature compensation is essential. In this paper, the effect of temperature on the force monitoring of steel cables using smart elasto-magneto-electric (EME) sensors was investigated experimentally. A back propagation (BP) neural network method is proposed to obtain a direct readout of the applied force in the engineering environment, involving less computational complexity. On the basis of the data measured in the experiment, an improved BP neural network model was established. The test result shows that, over a temperature range of approximately −10 °C to 60 °C, the maximum relative error in the force measurement is within ±0.9%. A polynomial fitting method was also implemented for comparison. It is concluded that the method based on a BP neural network can be more reliable, effective and robust, and can be extended to temperature compensation of other similar sensors.
基于弹磁（EM）效应的技术因其在在线结构的电缆应力/力监测方面的显著优势而受到越来越多的关注。环境温度的变化会影响钢构件的磁行为，从而导致传感器和测量系统结果出现误差。因此，温度补偿是必不可少的。本文通过实验研究了智能弹磁电（EME）传感器在钢缆力监测中的温度效应。提出了一种反向传播（BP）神经网络方法，以在工程环境中获得应用力的直接读数，涉及较少的计算复杂性。在实验测量的数据基础上，建立了一个改进的 BP 神经网络模型。试验结果表明，在约-10°C 至 60°C 的温度范围内，力测量的最大相对误差在±0.9%以内。还实现了多项式拟合方法进行比较。结论认为，基于 BP 神经网络的方法更可靠、有效和稳健，并可扩展到其他类似传感器的温度补偿。
