Spectral Demodulation of Fiber Bragg Grating Sensor Based on Deep Convolutional Neural Networks

Cao, Zihan; Zhang, Shengqi; Xia, Titi; Liu, Zhengyong; Li, Zhaohui

doi:10.1109/jlt.2022.3155253

Cited by 29 publications

(11 citation statements)

References 17 publications

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“…The sensing responses were measured three times, as displayed in Figure 8, where the error bar presents the standard deviation of the repeatable measurements. The averaged wavelength deviation of the pressure, temperature, and acceleration measurement is ~0.004, ~0.01, and ~0.02 nm, respectively, corresponding to a measurement accuracy of ~2 kPa, ~0.1°C, and ~0.14 g. It is worth noting that the measurement accuracy could be enhanced further by establishing more precise calibration and demodulating the FBG spectra using deep learning method (Cao et al, 2022), which we are currently working on.…”

Section: Discussionmentioning

confidence: 99%

“…With the integration of the interferometry-based fiber sensors, better algorithms such as deep learning method could be employed in the microprocessor of the proposed submarine interrogation system illustrated in Figure 2, which eventually retrieves the sensing signal in a smart approach. In a recent study (Cao et al, 2022), we have demonstrated that the spectrum of the FBG-based sensors can be directly demodulated by the deep convolutional neural network (DCNN) model, and good accuracy can be maintained; even the resolution of the hardware is reduced. This approach allows for the development of a compact and low-cost submarine sensing system, which is under investigation.…”

Section: Discussionmentioning

confidence: 99%

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Submarine Optical Fiber Sensing System for the Real-Time Monitoring of Depth, Vibration, and Temperature

et al. 2022

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Ocean observation becomes increasingly important as the ocean climate changes diversely and the marine disasters (such as tsunamis, typhoon, and earthquakes) occur frequently, which typically requires widespread and reliable monitoring techniques. In such a scenario, this paper presents a submarine optical fiber sensing system to realize real-time monitoring of the environmental parameters. The system consists of an undersea optical interrogation module together with multiple fiber Bragg grating (FBG)-based sensors, particularly for the measurement of depth, vibration, and temperature. The experimentally demonstrated sensitivities of the pressure, temperature, and vibration sensors are -1.993 nm/MPa, 0.08 nm/°C, and 0.139 nm/g (g = 9.8 m/s2), corresponding to the resolutions of 0.25 kPa, 0.006°C, and 0.004 g, respectively, based on the interrogation resolution of ~0.5 pm. To verify the feasibility and reliability of the proposed submarine sensing system, a prototype was developed and a proof test under the sea was conducted in an area close to Pearl River Estuary in China. The achieved results from the sea test show promising accuracy that is comparable to the commercially available electric-based sensors. Good characteristics of the surface water wave were observed by conducting the fast Fourier transform of the measured depth change, which shows a dominant frequency of ~0.25 Hz. The system provides the flexibility of replacing various optical fiber sensors easily and the capability of real-time monitoring in a remote way. The demonstrated submarine sensing system could find potential applications in real-time monitoring of the undersea ecosystem and the environmental evolution where multiparameter sensing is in demand.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Submarine Optical Fiber Sensing System for the Real-Time Monitoring of Depth, Vibration, and Temperature

et al. 2022

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“…It is noted that the dip wavelength of the interference spectrum was traced manually using ENLIGHT software during the measurement to ensure the accurate wavelength shift. In practical, better algorithms based on deep learning [22] are under investigation to solve the issue that dip wavelength shift exceeds one free spectral range (FSR). In an attempt to test the repeatability of the measurement, the response of dip wavelength to the pressure increasing and decreasing were measured repeatedly for three times.…”

Section: Pressure Measurementmentioning

confidence: 99%

Section: Pressure Measurementmentioning

confidence: 99%

Simultaneous Measurement of Temperature and Pressure Based on Fabry-Perot Interferometry for Marine Monitoring

Zhang

Mei

Xia

et al. 2022

Sensors

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The temperature and pressure of seawater are of great importance to investigate the environmental evolution for the research of ocean science. With this regard, we proposed and experimentally demonstrated a seawater temperature and pressure sensor realized by a polyimide (PI) tube-based Fabry-Perot interferometer (FPI) together with a fiber Bragg grating (FBG). Benefiting from the higher thermo-optical coefficient and larger elasticity of polymer than the fused silica fiber, the sensitivity of the sensor is largely improved. The FBG is used to compensate the cross effect of the temperature. The measured temperature and pressure sensitivities of the sensor are 18.910 nm/°C and −35.605 nm/MPa, respectively. Furthermore, the temperature and pressure information measured by the sensor can be achieved simultaneously using the sensitivity matrix method. In addition, the proposed sensor has advantages of easy fabrication, compact size, as well as capability of multiplexing and long-distance measurement, making it competitive and promising during the marine monitoring.

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“…Wavelet packet decomposition and Hilbert transform have been used [12] for accurate multi-peak detection. Spectral demodulation fitting has been presented [13] based on deep convolutional neural network training. Such improvements could lead to fitting specific shapes of the FBG spectral peaks in order to have reliable FBGs recognition and higher spectral utilization in multisensory applications.…”

Section: Introductionmentioning

confidence: 99%

SNR-based dynamic statistical threshold detection of FBG spectral peaks

Cibira¹,

Glesk²

2022

Preprint

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This paper targets a dynamic statistical threshold detection algorithm of fiber Bragg grating (FBG) spectral peaks at the presence of changing Signal-to-Noise Ratio (<i>SNR</i>) in an optical fiber of a sensing application. The proposed post-demodulation <i>SNR</i>-based detection implements sliding window technique. Its detection threshold is adapted by the targeted probability of false alarms and by background noise statistics. The proposed detection algorithm is independent of FBG spectral peaks shapes and simple enough computationally to implement. It has been demonstrated and validated using sensor network with a deployed group of FBG-based sensors, by implementing simplified sliding window technique. When the adjacent FBG spectral peaks overlap partially, it provides a high degree of certainty in rejecting false FBG detection. The algorithm can be used for „spectral windowing“ or precise measurement of FBG spectral peaks parameters, especially in densely populated FBG sensor networks. This can lead to a significantly higher spectral bandwidth utilization in FBG sensing applications.

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Spectral Demodulation of Fiber Bragg Grating Sensor Based on Deep Convolutional Neural Networks

Cited by 29 publications

References 17 publications

Submarine Optical Fiber Sensing System for the Real-Time Monitoring of Depth, Vibration, and Temperature

Submarine Optical Fiber Sensing System for the Real-Time Monitoring of Depth, Vibration, and Temperature

Simultaneous Measurement of Temperature and Pressure Based on Fabry-Perot Interferometry for Marine Monitoring

SNR-based dynamic statistical threshold detection of FBG spectral peaks

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