Capacity and capability enhancements of FBG sensor system by utilizing intensity and WDM detection technique

Yeh, Chien Hung; Zhuang, Yuan; Tsai, Ning; Chow, Chi‐Wai

doi:10.1088/1361-665x/aa5747

Cited by 18 publications

(17 citation statements)

References 14 publications

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“…To avoid overlaps of FBG central wavelength to interrogate sensors, the number of FBGs it limited to 10–100 s in the same fiber when using WDM [25]. Moreover, if the fiber optic connected by FBGs is damaged, the whole demodulation channel will be damaged [27,28]. Sometimes it will affect the data measurement of other channels and reduce the accuracy of sensor measurement.…”

Section: Working Principle and Technical Characteristicsmentioning

confidence: 99%

Fiber Bragg Gratings Sensors for Aircraft Wing Shape Measurement: Recent Applications and Technical Analysis

Chen

2018

Sensors

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The safety monitoring and tracking of aircraft is becoming more and more important. Under aerodynamic loading, the aircraft wing will produce large bending and torsional deformation, which seriously affects the safety of aircraft. The variation of load on the aircraft wing directly affects the ground observation performance of the aircraft baseline. To compensate for baseline deformations caused by wing deformations, it is necessary to accurately obtain the deformation of the wing shape. The traditional aircraft wing shape measurement methods cannot meet the requirements of small size, light weight, low cost, anti-electromagnetic interference, and adapting to complex environment at the same time, the fiber optic sensing technology for aircraft wing shape measurement has been gradually proved to be a real time and online dynamic measurement method with many excellent characteristics. The principle technical characteristics and bonding technology of fiber Bragg grating sensors (FBGs) are reviewed in this paper. The advantages and disadvantages of other measurement methods are compared and analyzed and the application status of FBG sensing technology for aircraft wing shape measurement is emphatically analyzed. Finally, comprehensive suggestions for improving the accuracy of aircraft wing shape measurement based on FBG sensing technology is put forward.

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Section: Working Principle and Technical Characteristicsmentioning

confidence: 99%

Fiber Bragg Gratings Sensors for Aircraft Wing Shape Measurement: Recent Applications and Technical Analysis

Chen

2018

Sensors

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“…Finally, the modulation mechanism of the sensors is intimately related to the multiplexing technique which must be used in order to interrogate several of them in the same network. Previous reviews presenting the state of the art regarding the different types of FOS can be found in [1][2][3][4][5][6][7].…”

Section: Fiber-optic Sensor Network-backgroundmentioning

confidence: 99%

Truly remote fiber optic sensor networks

Soto

López-Amo

2019

J. Phys. Photonics

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An overview of truly remote fiber optic sensors is presented in this work. It starts with a brief introduction of fiber optic sensor networks, showing their advantages and multiple applications. Then, the definition of truly remote networks is provided, and their main challenges discussed, such as increasing the sensing distance and the number of sensors interrogated. Several multiplexing techniques have been compared, such as wavelength, time and coherence division multiplexing. In relation to this, the most recent works showing multi wavelength fiber lasers for wavelength division multiplexing have been grouped and their versatility analyzed. Finally, recent and relevant truly remote fiber optic networks have been gathered and some of the most representative schemes explained in detail, comparing their multiplexing capability and the remoteness of the monitored sensors. Random distributed feedback fiber lasers form part of a number of these schemes, proving the suitability of this type of lasers for their use in ultra-long truly remote sensing applications.

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“…Recently, intensity wavelength division multiplexing (IWDM) techniques have been proposed to improve the multiplexing ability of the sensor system, where the reflection spectra of FBG sensors in the sensor system are allowed to overlap [9]. Thus, IWDM is used to enhance the multiplexing capacity of the FBG sensor network and to enable the sensor system to contain more than twice as many FBGs as traditional WDM techniques [9][10][11]. However, the overlapping spectra of FBG sensors can cause cross talk between the adjacent FBGs and can make it difficult for the traditional wavelength detection techniques to identify the sensing signal of each FBG sensor.…”

Section: Introductionmentioning

confidence: 99%

Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network

Manie

Peng

et al. 2020

Sensors

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In this paper, for an intensity wavelength division multiplexing (IWDM)-based multipoint fiber Bragg grating (FBG) sensor network, an effective strain sensing signal measurement method, called a long short-term memory (LSTM) machine learning algorithm, integrated with data de-noising techniques is proposed. These are considered extremely accurate for the prediction of very complex problems. Four ports of an optical coupler with distinct output power ratios of 70%, 60%, 40%, and 30% have been used in the proposed distributed IWDM-based FBG sensor network to connect a number of FBG sensors for strain sensing. In an IWDM-based FBG sensor network, distinct power ratios of coupler ports can contain distinct powers or intensities. However, unstable output power in the sensor system due to random noise, harsh environments, aging of the equipment, or other environmental factors can introduce fluctuations and noise to the spectra of the FBGs, which makes it hard to distinguish the sensing signals of FBGs from the noise signals. As a result, noise reduction and signal processing methods play a significant role in enhancing the capability of strain sensing. Thus, to reduce the noise, to improve the signal-to-noise ratio, and to accurately measure the sensing signal of FBGs, we proposed a long short-term memory (LSTM) deep learning algorithm integrated with discrete waveform transform (DWT) data smoother (de-noising) techniques. The DWT data de-noising methods are important techniques for analyzing and de-noising the sensor signals, and it further improves the strain sensing signal measurement accuracy of the LSTM model. Thus, after de-noising the sensor data, these data are fed into the LSTM model to measure the sensing signal of each FBG. The experimental results prove that the integration of LSTM with the DWT data de-noising technique achieved better sensing signal measurement accuracy, even in noisy data or environments. Therefore, the proposed IWDM-based FBG sensor network can accurately sense the signal of strain, even in bad or noisy environments; can increase the number of FBG sensors multiplexed in the sensor system; and can enhance the capacity of the sensor system.

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Capacity and capability enhancements of FBG sensor system by utilizing intensity and WDM detection technique

Cited by 18 publications

References 14 publications

Fiber Bragg Gratings Sensors for Aircraft Wing Shape Measurement: Recent Applications and Technical Analysis

Fiber Bragg Gratings Sensors for Aircraft Wing Shape Measurement: Recent Applications and Technical Analysis

Truly remote fiber optic sensor networks

Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network

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