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A Novel Optical Fiber Displacement Sensor of Wider Measurement Range Based on Neural Network

机译:基于神经网络的新型宽量程光纤位移传感器

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By studying on the output characteristics of random type optical fiber sensor and semicircular type optical fiber sensor, the ratio of the two output signals was used as the output signal of the whole system. Then the measurement range was enlarged, the linearity was improved, and the errors of reflective and absorbent changing of target surface are automatically compensated. Meantime, an optical fiber sensor model of correcting static error based on BP artificial neural network(ANN) is set up. So the intrinsic errors such as effects of fluctuations in the light, circuit excursion, the intensity losses in the fiber lines and the additional losses in the receiving fiber caused by bends are eliminated. By discussing in theory and experiment, the error of nonlinear is 2.9%, the measuring range reaches to 5 6mm and the relative accuracy is 2%.And this sensor has such characteristics as no electromagnetic interference, simple construction, high sensitivity, good accuracy and stability. Also the multi-point sensor system can be used to on-line and non-touch monitor in working locales.
机译:通过研究随机型光纤传感器和半圆形型光纤传感器的输出特性,将两个输出信号之比作为整个系统的输出信号。然后扩大了测量范围,改善了线性度,并自动补偿了目标表面反射和吸收剂变化的误差。同时,建立了基于BP神经网络的光纤静态纠错传感器模型。因此,消除了固有的误差,例如光的波动,电路偏移,光纤线路中的强度损耗以及由于弯曲而导致的接收光纤中的附加损耗等。经过理论和实验讨论,该传感器的非线性误差为2.9%,测量范围达到5 6mm,相对精度为2%,具有无电磁干扰,结构简单,灵敏度高,精度高等特点。稳定。此外,多点传感器系统还可用于在工作区域中进行在线和非触摸式监控。

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