Abstract
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.
Highlights
Due to the primary appealing characteristics of high multiplexing capability, low price, small size, low noise interference, and remote sensing suitability, fiber Bragg grating (FBG) sensors are commonly used for strain, temperature, vibration, and other measurements [1,2,3]
To train the long short-term memory (LSTM) algorithm, the preprocessed reflection spectra of the FBGs are used as inputs to the LSTM and the corresponding peak wavelengths of FBGs are used as target values
The prediction performance of our proposed model is evaluated through root mean square error (RMSE)
Summary
Due to the primary appealing characteristics of high multiplexing capability, low price, small size, low noise interference, and remote sensing suitability, fiber Bragg grating (FBG) sensors are commonly used for strain, temperature, vibration, and other measurements [1,2,3]. In a distributed sensor system, a number of FBG sensors can be multiplexed in the fiber cable using the wavelength division multiplexing (WDM) method. In a traditional WDM, a unique spectral operational region is assigned to each FBG sensor and the reflection spectra of contiguous FBGs sensors cannot be allowed to overlap. This extremely limits the numbers of FBG sensors in the sensor system. 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]
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