A De-Noising Algorithm Based on EEMD in Raman-Based Distributed Temperature Sensor

Pan, Liang; Liu, Kun; Jiang, Junfeng; Cheng, Mengqi; Miao, Tian

doi:10.1109/jsen.2016.2623860

Cited by 24 publications

(5 citation statements)

References 15 publications

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“…Information entropy can describe the average uncertainty of probabilistic systems [36]- [38]. If the probability distribution p(x i ), i = 1, 2,. .…”

Section: B Eemd Energy Entropymentioning

confidence: 99%

Coal-Gangue Interface Detection Based on Ensemble Empirical Mode Decomposition Energy Entropy

Liu

Wang

et al. 2021

IEEE Access

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To realize the unmanned automation of the full mechanized caving, the bottleneck problem of coal-gangue interface detection in top coal caving must be solved first. Targeting coal-gangue interface detection on fully mechanized mining face, an alternative scheme to detect coal-gangue interface based on vibration signal analysis of the tail boom support of the longwall mining machine. It is found that when coal and gangue fall, the characteristics of vibration signals generated by coal and gangue shocking the tail boom are different. First, EEMD algorithm is used to decompose the original vibration signals into intrinsic mode functions (IMFs). Each IMF represents the distribution of energy from high to low. EEMD algorithm can restrain the mode mixing phenomenon caused by empirical mode decomposition (EMD). The energy of vibration signals will change in different frequency bands when the top-coal fall down or the coal-gangue fall down. According the information theory, we define EEMD energy entropy to describe this change. Experimental results show that EEMD energy entropy of top-coal caving is always greater than that of coal-gangue caving. Thus, the Mahalanobis distance metric method based on EEMD energy entropy is proposed for coal-gangue interface detection. The results show the proposed method can be used as a robust empirical method for coal-gangue interface detection.

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“…Information entropy can describe the average uncertainty of probabilistic systems [36]- [38]. If the probability distribution p(x i ), i = 1, 2,. .…”

Section: B Eemd Energy Entropymentioning

confidence: 99%

Coal-Gangue Interface Detection Based on Ensemble Empirical Mode Decomposition Energy Entropy

Liu

Wang

et al. 2021

IEEE Access

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“…These algorithms decompose the data according to a particular criterion and separate the noise from the data. They include the short-time Fourier transform [33], the empirical modal decomposition [34], and the Wavelet denoising [35], [36], [37]. But these denoising algorithms have the problems of requiring manual adjustment of parameters, having poor denoising performance, and deteriorating the spatial resolution, a key system metric that is susceptible to degradation during the denoising process.…”

Section: Introductionmentioning

confidence: 99%

High Precision Raman Distributed Fiber Sensing Using Residual Composite Dual-Convolutional Neural Network

Guo,

Li,

Xue

et al. 2024

J. Lightwave Technol.

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Raman distributed optical fiber sensing has the unique ability to measure the spatially distributed profile of temperature that are of great interest to numerous field applications. However, the sensing performance is severely limited by the signal-to-noise ratio (SNR). The existing SNR enhancement schemes have drawbacks such as increased system complexity, degradation of sensor performance metrics such as spatial resolution, poor denoising performance, etc. Here, we report the Raman residual composite dual-convolutional neural network (RRCDNet), a novel convolutional neural networkbased denoising model for one-dimensional signals specifically tailored to Raman distributed fiber sensing. The RRCDNetenhanced Raman distributed fiber sensor system dramatically improves the temperature precision by more than a factor of 100, from 7.57°C to 0.06°C, without hardware modification or degradation of other performance metrics. At the same time, RRCDNet can also enhance other optical fiber sensor systems with one-dimensional signals, such as Rayleigh and Brillouin sensing systems.

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“…EMD can achieve an automatic decomposition without selecting mother 2 of 16 wavelets and decomposition levels in advance [8]. Nevertheless, EMD still has some other deficiencies, such as mode mixing and end effect [9,10]. Chan et al [11] proposed a hybrid method called ensemble EMD (EEMD) to overcome mode mixing and avoid end effect in EMD.…”

Section: Introductionmentioning

confidence: 99%

An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition

Zhou

et al. 2019

Energies

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To extract partial discharge (PD) signals from white noise efficiently, this paper proposes a denoising method for PD signals, named adaptive short-time singular value decomposition (ASTSVD). First, a sliding window was moved along the time axis of a PD signal to cut a whole signal into segments with overlaps. The singular value decomposition (SVD) method was then applied to each segment to obtain its singular value sequence. The minimum description length (MDL) criterion was used to determine the number of effective singular values automatically. Then, the selected singular values of each signal segment were used to reconstruct the noise-free signal segment, from which the denoised PD signal was obtained. To evaluate ASTSVD, we applied ASTSVD and two other methods on simulated, laboratory-measured, and field-detected noisy PD signals, respectively. Compared to the other two methods, the denoised PD signals of ASTSVD contain less residual noise and exhibit smaller waveform distortion.

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A De-Noising Algorithm Based on EEMD in Raman-Based Distributed Temperature Sensor

Cited by 24 publications

References 15 publications

Coal-Gangue Interface Detection Based on Ensemble Empirical Mode Decomposition Energy Entropy

Coal-Gangue Interface Detection Based on Ensemble Empirical Mode Decomposition Energy Entropy

High Precision Raman Distributed Fiber Sensing Using Residual Composite Dual-Convolutional Neural Network

An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition

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