School of Engineering and Physical Sciences, Heriot-Watt University Malaysia, Putrajaya 62200, Malaysia.
Institute of Power Engineering, Universiti Tenaga Nasional, Kajang 43000, Malaysia.
Sensors (Basel). 2022 Mar 31;22(7):2677. doi: 10.3390/s22072677.
In this paper, we studied the possibility of increasing the Brillouin frequency shift (BFS) detection accuracy in distributed fibre-optic sensors by the separate and joint use of different algorithms for finding the spectral maximum: Lorentzian curve fitting (LCF, including the Levenberg-Marquardt (LM) method), the backward correlation technique (BWC) and a machine learning algorithm, the generalized linear model (GLM). The study was carried out on real spectra subjected to the subsequent addition of extreme digital noise. The precision and accuracy of the LM and BWC methods were studied by varying the signal-to-noise ratios (SNRs) and by incorporating the GLM method into the processing steps. It was found that the use of methods in sequence gives a gain in the accuracy of determining the sensor temperature from tenths to several degrees Celsius (or MHz in BFS scale), which is manifested for signal-to-noise ratios within 0 to 20 dB. We have found out that the double processing (BWC + GLM) is more effective for positive SNR values (in dB): it gives a gain in BFS measurement precision near 0.4 °C (428 kHz or 9.3 με); for BWC + GLM, the difference of precisions between single and double processing for SNRs below 2.6 dB is about 1.5 °C (1.6 MHz or 35 με). In this case, double processing is more effective for all SNRs. The described technique's potential application in structural health monitoring (SHM) of concrete objects and different areas in metrology and sensing were also discussed.
在本文中，我们研究了通过分别和联合使用不同的算法来提高分布式光纤传感器中的布里渊频移（BFS）检测精度的可能性，这些算法用于寻找光谱最大值：洛伦兹曲线拟合（LCF，包括 Levenberg-Marquardt（LM）方法）、后向相关技术（BWC）和机器学习算法——广义线性模型（GLM）。该研究是在对真实光谱进行后续添加极端数字噪声的基础上进行的。通过改变信噪比（SNR）并将 GLM 方法纳入处理步骤，研究了 LM 和 BWC 方法的精度和准确性。结果发现，按顺序使用这些方法可以提高从十分之一到几度的传感器温度的确定精度（或 BFS 尺度上的 MHz），这在 0 到 20 dB 的信噪比范围内表现出来。我们发现，对于正的 SNR 值（在 dB 中），双重处理（BWC+GLM）更为有效：它可以在接近 0°C（428 kHz 或 9.3 με）的 BFS 测量精度上带来增益；对于 BWC+GLM，在 SNR 低于 2.6 dB 的情况下，单处理和双处理之间的精度差异约为 1.5°C（1.6 MHz 或 35 με）。在这种情况下，对于所有 SNR，双重处理都更有效。还讨论了该技术在混凝土物体的结构健康监测（SHM）以及计量和传感领域的潜在应用。
