基于深度学习的光纤光栅锅炉状态监测研究

Research on boiler monitoring of FBG based on deep learning

为实时监控锅炉工作的健康状态,精准获取锅炉管道的温度和受力情况,提出基于深度学习的光纤光栅锅炉状态监测技术。设计了一种双光纤光栅级联封装的传感器结构及其固定方法,用以提高传感器的测量性能。构建特征融合并行transformer回归预测模型对传感器的温度和应变信号进行处理,实现了对传感单元温度和应变的准确识别。实验结果表明,传感器中的两光栅对温度的灵敏度为12.31 pm/℃和11.63 pm/℃,对应变的灵敏度为1.2 pm/με和0,消除温度对应变测量的影响,具有温度补偿作用。通过引入深度学习算法,解决了高温环境下光纤光栅对温度和应变混合敏感存在高阶混合项而难以求解的难题,模型的决定系数大于0.9、平均绝对误差和均方误差分别为0.23和0.31,有效地提高了传感器对温度和应力的识别精度。综上所述,该技术实现了高温环境下温度和应变的准确测量,弥补了传统测量方式高温失效、单点测量等不足,为锅炉工作健康状态的实时监控提供了有效的解决方案。

To monitor the health status of the boiler in real-time and accurately obtain the temperature and stress situation of the boiler pipeline,a fiber optic grating boiler status monitoring technology based on deep learning is proposed.A sensor structure with dual fiber Bragg grating cascaded packaging and its fixing method have been designed to improve the measurement performance of the sensor.A feature fusion parallel transformer regression prediction model was constructed to process the temperature and strain signals of sensors,achieving accurate recognition of the temperature and strain of sensing units.The experimental results show that the sensitivity of the two gratings in the sensor to temperature is 12.31 pm/℃and 11.63 pm/℃,and the sensitivity to strain is 1.2 pm/μεand 0,eliminating the influence of temperature on strain measurement,with temperature compensation effect.By introducing deep learning algorithms,the difficult problem of high-order mixing terms in the sensitivity of fiber Bragg gratings to temperature and strain mixing in high-temperature environments has been solved.The model′s coefficient of determination is greater than 0.9,and the average absolute error and mean square error are 0.23 and 0.31,respectively,effectively improving the sensor′s recognition accuracy for temperature and stress.In summary,this technology has achieved accurate measurement of temperature and strain in high-temperature environments,making up for the shortcomings of traditional measurement methods such as high-temperature failure and single point measurement.It provides an effective solution for real-time monitoring of boiler working health status.