An Ameliorated Denoising Scheme Based on Deep Learning for Φ-OTDR System With 41-km Detection Range

Li, Sichen; Liu, Kun; Jiang, Junfeng; Xu, Tianhua; Ding, Zhenyang; Sun, Zhihui; Huang, Yuelang; Xue, Kang; Jin, Xibo

doi:10.1109/jsen.2022.3202963

Cited by 13 publications

(2 citation statements)

References 42 publications

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“…The data collected typically include a data matrix consisting of the number of Rayleigh backscattered traces and the sampling points for each trace, with the horizontal direction of each matrix representing the spatial domain and the vertical direction representing the temporal domain [37]. The length of the data in the spatial domain is determined by the length of the sensing fiber, and the length in the temporal domain is 0.05 s and contains 500 Rayleigh backscattered traces, with each detection pulse acquiring one Rayleigh backscattered trace, as shown in Figure 3.…”

Section: Vibration Data Preprocessingmentioning

confidence: 99%

Multi-Event Location Denoising Scheme for φ-OTDR Based on FFDNet Network

Yang,

Li,

et al. 2023

Photonics

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In order to improve the signal-to-noise ratio (SNR) of vibration sensing in the phase-sensitive optical time-domain reflectometer (φ-OTDR) system, a fiber sensing signal processing method based on the FFDNet convolutional neural network is proposed in this paper. In the network, the concept of residual learning is introduced, which involves constructing a residual mapping and utilizing multi-layer convolutional neural networks to learn the noise distribution present in the original image. The denoised result can be obtained by subtracting the learned noise from the original image. We have built a φ-OTDR system based on coherent detection, using three PZTs as simulated vibration sources and a series of experiments at 200 Hz, with each experiment simulating a single vibration event or multiple vibration events by setting different intensities. The experimental results demonstrate that the FFDNet based fiber optic sensing signal processing method enhances the SNR to 37.84 dB, 37.11 dB, and 37.31 dB, respectively, while preserving vibration signal details more effectively than wavelet denoising and Gaussian filtering techniques. The trained FFDNet model has great potential for improving the performance of the φ-OTDR system and has some practical application value.

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Section: Vibration Data Preprocessingmentioning

confidence: 99%

Multi-Event Location Denoising Scheme for φ-OTDR Based on FFDNet Network

Yang,

Li,

et al. 2023

Photonics

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“…Wu H. et al used bidirectional long–short-term memory based on one-dimensional convolution neural networks to improve the vibration event recognition rate of a distributed acoustic sensor system to 98.6% [ 33 ]. Li S. et al proposed a CNN image denoising model that can effectively reduce the noise in the phi-OTDR system, reduce the denoising time, and improve the denoising effect [ 34 ]. This model was validated on a 41 km sensing fiber.…”

Section: Introductionmentioning

confidence: 99%

Optical Frequency Domain Reflectometry Based on Multilayer Perceptron

Yin

Zhu

Liu

et al. 2023

Sensors

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We proposed an optical frequency domain reflectometry based on a multilayer perceptron. A classification multilayer perceptron was applied to train and grasp the fingerprint features of Rayleigh scattering spectrum in the optical fiber. The training set was constructed by moving the reference spectrum and adding the supplementary spectrum. Strain measurement was employed to verify the feasibility of the method. Compared with the traditional cross-correlation algorithm, the multilayer perceptron achieves a larger measurement range, better measurement accuracy, and is less time-consuming. To our knowledge, this is the first time that machine learning has been introduced into an optical frequency domain reflectometry system. Such thoughts and results would bring new knowledge and optimization to the optical frequency domain reflectometer system.

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