SNR enhancement of a Raman distributed temperature sensor using partial window-based non local means method

Malakzadeh, Abdollah; Didar, Mohammad; Mansoursamaei, Mohsen

doi:10.1007/s11082-021-02762-w

Cited by 15 publications

(6 citation statements)

References 30 publications

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“…The light intensity of anti Stokes light changes obviously with temperature, while the light intensity of Stokes light does not change obviously with temperature. At present, the most commonly used temperature demodulation scheme is to use wavelength division multiplexer to detect Stokes light and anti Stokes light respectively with photodetectors, and then calculate the ratio of the two to obtain temperature information [12].…”

Section: Measurement Principle Of Dts Based On Raman Scatteringmentioning

confidence: 99%

Automatic detection of the height of poured concrete via fiber-optic temperature sensing

LI,

YANG,

ZHU

et al. 2024

Preprint

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In the process of bridge foundation construction, the height of poured concrete cannot be measured automatically, which depends on manual operation and construction interruption. This paper proposes an automatic detection technology of the height of poured concrete via fiber-optic temperature sensing, which use the small temperature difference between concrete and air medium in the process of concrete pouring. The median filtering method is used to process the temperature position curve to obtain a signal curve similar to a step signal. The step functions with different periods are constructed, and the correlation coefficient curve is obtained by cross-correlation operation between the obtained temperature curve and the step functions with different periods. The variable step size search method is used to improve computational efficiency. Finally, the correlation coefficient curve is processed by peak seeking to realize the automatic discrimination of concrete level elevation. This technology has been successfully applied to the diaphragm wall project of Zhang-Jing-Gao Yangtze River Bridge. Limited by the spatial resolution of the temperature measurement system, the resolution of the liquid level elevation is ± 0.5m, and the automatic judgment of the liquid level elevation is consistent with the actual measurement results judged by the temperature nephogram, indicating that the algorithm has good accuracy, and the results of 30 operations at the same time are consistent, indicating that the algorithm has good stability.

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Section: Measurement Principle Of Dts Based On Raman Scatteringmentioning

confidence: 99%

Automatic detection of the height of poured concrete via fiber-optic temperature sensing

LI,

YANG,

ZHU

et al. 2024

Preprint

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“…IPMs reported that they can achieve a higher SNR than NLM, but they are usually based on noise level estimation [29]. Therefore, many NLM optimization algorithms have been proposed, based on principal component analysis (PCA) [30], noise estimation [27], modified [31], partial window [10,31,32], and signal estimation [33]. Recently, adopting the Grey Wolf algorithm and a neural network to optimize NLM has also become an option [34][35][36].…”

Section: Related Workmentioning

confidence: 99%

The Optimization of a Pipeline Temperature Monitoring Method Based on Non-Local Means with the Black Widow Optimization Algorithm

Lou,

Wang,

Sima

et al. 2023

Energies

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The accuracy of pipeline temperature monitoring using the Brillouin Optical Time Domain Analysis system depends on the Brillouin Gain Spectrum in the Brillouin Optical Time Domain Analysis system. The Non-Local Means noise reduction algorithm, due to its ability to use the data patterns available within the two-dimensional measurement data space, has been used to improve the Brillouin Gain Spectrum in the Brillouin Optical Time Domain Analysis system. This paper studies a new Non-Local Means algorithm optimized through the Black Widow Optimization Algorithm, in view of the unreasonable selection of smoothing parameters in other Non-Local Means algorithms. The field test demonstrates that, the new algorithm, when compared to other Non-Local Means methods, excels in preserving the detailed information within the Brillouin Gain Spectrum. It successfully restores the fundamental shape and essential characteristics of the Brillouin Gain Spectrum. Notably, at the 25 km fiber end, it achieves a 3 dB higher Signal-to-Noise Ratio compared to other Non-Local Means noise reduction algorithms. Furthermore, the Brillouin Gain Spectrum values exhibit increases of 9.4% in Root Mean Square Error, 12.5% in Sum of Squares Error, and 10% in Full Width at Half Maximum. The improved method has a better denoising effect and broad application prospects in pipeline safety.

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“…At present, the development speed of intelligent sensors is getting faster and faster. erefore, temperature sensors have been transformed from traditional analog to digital and intelligent [1]. Temperature sensors mainly ensure the accuracy of temperature data collection through the cooperation of hardware and software, saving a lot of design and manufacturing costs, so they have been widely used in various social fields [2].…”

Section: Introductionmentioning

confidence: 99%

Study on Temperature Sensor Data Anomaly Diagnosis Method Based on Deep Neural Network

Jing

Wang

Zhang

2022

Scientific Programming

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In order to solve the problems of low accuracy and low efficiency of traditional sensor data anomaly diagnosis methods, a new temperature sensor data anomaly diagnosis method based on deep neural network is proposed in this paper. Firstly, the temperature sensor data in a running cycle is collected, and the characteristics of the temperature sensor data are extracted by the sliding window technology. Secondly, based on the feature extraction results, a deep neural network model for anomaly diagnosis of temperature sensor data is constructed. The feature data are input into the model, and the result obtained is the diagnosis result. Finally, the simulation comparison experiment is carried out. The experimental results show that the error rate of feature extraction of temperature sensor data in this method changes between −2.1% and 5.9%, the diagnosis accuracy remains above 95%, the average diagnosis time is only 59 ms, and the diagnosis efficiency is high.

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SNR enhancement of a Raman distributed temperature sensor using partial window-based non local means method

Cited by 15 publications

References 30 publications

Automatic detection of the height of poured concrete via fiber-optic temperature sensing

Automatic detection of the height of poured concrete via fiber-optic temperature sensing

The Optimization of a Pipeline Temperature Monitoring Method Based on Non-Local Means with the Black Widow Optimization Algorithm

Study on Temperature Sensor Data Anomaly Diagnosis Method Based on Deep Neural Network

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