| 法布里⁃珀罗游标光谱信号的深度学习解调 |
| Deep learning⁃based demodulation of Fabry⁃Pérot vernier spectral signals |
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| 中文摘要: |
| 为提升法布里?珀罗（Fabry?Pérot， F?P）传感器游标光谱信号解调的准确性，提出基于深度学习的光谱数据直接解调方法。首先对光谱数据进行预处理，将复杂的游标光谱信息转化为卷积神经网络（Convolutional Neural Network， CNN）可以处理的数据格式，然后采用深度学习模型对预处理后的完整光谱数据进行训练和测试，并利用卷积神经网络对光谱数据进行特征提取和分类，最终实现待测信号的准确解调。使用灵敏度为112.5 nm / MPa的双腔法布里?珀罗传感器采集光谱数据，并开展信号解调实验，结果表明：CNN模型对未知光谱进行10折（fold）交叉验证的平均准确率为92.49%，均方根误差RRMSE（Root Mean Square Error， RMSE）为0.039 2 MPa，相对误差的平均值为3.31%；卷积神经网络?长短期记忆（Convolutional Neural Network?Long Short Term Memory， CNN?LSTM）模型对未知光谱进行10折交叉验证的平均准确率为96.98%，RRMSE为0.039 0 MPa，相对误差的平均值为3.28%。基于CNN?LSTM模型的方法仅通过解调256个采样点的数据就实现了较高准确度，具有便捷、高效的优点，为推动光谱信号解调领域发展提供了有效的技术途径，为开发智能光学传感系统提供了重要参考。 |
| 英文摘要: |
| To enhance the demodulation accuracy of vernier spectral signals in Fabry?Pérot (F?P) sensors, this study proposes a direct deep learning?based demodulation method for spectral data. The method involves preprocessing spectral data to convert complex vernier spectral information into formats compatible with Convolutional Neural Network (CNN), followed by training and testing deep learning models on the processed full?spectrum data. The CNN architecture was employed for feature extraction and classification of spectral data, enabling accurate demodulation of target signals. Experimental validation was conducted utilizing spectral data collected from a dual?cavity F?P sensor with 112.5 nm / MPa sensitivity. The results demonstrate that the CNN model achieved an average accuracy of 92.49% with 10?fold cross?validation, accompanied by a Root Mean Square Error (RMSE) of 0.039 2 MPa and a mean relative error of 3.31%. The hybrid Convolutional Neural Network?Long Short Term Memory (CNN?LSTM) model exhibited superior performance with an average accuracy of 96.98%, an RMSE of 0.039 0 MPa, and a mean relative error of 3.28%. Notably, the CNN?LSTM approach attained high precision using only 256 sampled data points, demonstrating remarkable efficiency. This method provides an effective technical pathway for advancing spectral signal demodulation technology, offering significant reference value for developing intelligent optical sensing systems. |
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| 中文关键词: 光纤传感器 法布里⁃珀罗干涉仪 光谱解调 深度学习 游标效应 |
| 英文关键词:optical fiber sensor Fabry⁃Pérot interferometer spectral demodulation deep learning vernier effect |
| 基金项目: |
| DOI：10.11823/j.issn.1674-5795.2025.03.06 |
| 引用本文：王桧, 赵起超, 王昊琦, 邵志强, 肖爽, 刘彬.法布里⁃珀罗游标光谱信号的深度学习解调[J].计测技术,2025,45(3):70~77. |
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