Denoising method based on VMD-PCC in φ-OTDR system

Mao, Bangning; Bu, Zehua; Xu, Bin; Gong, Hui; Li, Yang; Wang, Hailong; Kang, Juan; Jin, Shangzhong; Chen, Zhao

doi:10.1016/j.yofte.2022.103081

Cited by 12 publications

(3 citation statements)

References 16 publications

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“…In this paper, the five scenarios of wind, trampling, etc., are decomposed into five Intrinsic Mode Functions (IMFs) using the Variational Mode Decomposition (VMD) method, and the specific decomposition results are shown in Figure 6. During the decomposition process, a low number of decomposition levels can affect the recognition accuracy, while a high number of decomposition levels can lead to mode overlap [17] , Therefore, in this study, five decomposition levels were chosen as the optimal setting.…”

Section: Resultsmentioning

confidence: 99%

Distributed φ-OTDR signal classification based on VMD and SVM

Jia,

Cao,

Xie

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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With the development of distributed fiber optic sensing, the recognition of different vibration modes has become increasingly important. In this paper, a distributed external-heterodyne φ-OTDR system is used for outdoor vibration acquisition and mode recognition. In the event recognition experiment, feature vectors are obtained using the Variational Mode Decomposition (VMD) algorithm. Support Vector Machine (SVM) is then used to classify the input feature vectors accordingly. Through this experimental method, it is possible to accurately identify five types of vibrations: stepping, tapping, wind blowing, passing subway, and passing car. Compared to traditional methods, the accuracy has improved from 72.50% to 91.25%, demonstrating promising application prospects.

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Section: Resultsmentioning

confidence: 99%

Distributed φ-OTDR signal classification based on VMD and SVM

Jia,

Cao,

Xie

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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“…The higher the value of the PCC, the higher the correlation between the two variables. As shown in Table 5 , the correlation coefficients were categorized as no correlation, weak correlation, moderate correlation, significant correlation and strong correlation 35 . In this experiment, the threshold for the PCC is established at 0.4.…”

Section: Resultsmentioning

confidence: 99%

Detection of incipient rotor unbalance fault based on the RIME-VMD and modified-WKN

Wang,

Hu,

Wang

2024

Sci Rep

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Due to the high incidence and inconspicuous initial characteristics of rotor unbalance faults, the detection of incipient unbalance faults is becoming a very challenging problem. In this paper, a new method of small rotor unbalance fault diagnosis based on RIME-VMD and modified wavelet kernel network (modified-WKN) is proposed. Firstly, in order to extract the small unbalance fault information from the vibration signals with low signal-to-noise ratio (SNR) more efficiently, the RIME algorithm is used to search for the optimal location of the penalty factor and decomposition layer in the variable mode decomposition (VMD). Secondly, the most relevant decomposition components to the small unbalance fault information are selected by using Pearson Correlation Coefficients and utilized to reconstruct the signal. Finally, the modified-WKN diagnostic model that is used for multi-sensor data fusion is constructed. The model can acquire features of vibration signals from multiple position sensors, which enhances the ability of the modified WKN diagnostic model to deal with incipient fault modes. Based on the experimental analysis of rotor unbalance fault datasets with different SNRs, it is verified that the detection performance of the proposed method is better than the traditional WKN and VMD-WKN methods. Specifically, the proposed method is more sensitive to the initial unbalance faults.

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“…The degree of correlation between two time-series is often measured by the Pearson correlation coefficient, denoted as ρ, which takes values in the range of [−1, 1] [21]. After VMD, the correlation coefficient between each signal component and the original signal can be used to evaluate the contribution of each component, and the main components can be determined through comparison [22].…”

Section: Correlation Analysismentioning

confidence: 99%

Correlation analysis of cavitation-induced pressure pulsation and vibration in a bulb turbine

Li,

Feng,

Zhu

et al. 2023

J Hydrodyn

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Cavitation is one of the primary factors leading to the destabilisation of tubular turbines. To enhance their stability, this study examines the effects of runner cavitation on draft tube pressure fluctuations and vibration in tubular turbines through experimental methods. With varying cavitation coefficients, a synchronous test system, including a high-speed camera, vibration acceleration sensors and pressure pulsation sensors, is applied to obtain cavitation images of the runner, and vibration and internal fluid pressure pulsation data of the draft tube. The results show that the correlated component of pressure pulsation signals during the cavitation process is the synchronous pressure pulsation of 16 fn. With the development of cavitation, the amplitude of synchronous pressure pulsation increases first and then decreases. Cavitation enhances the high-frequency vibration of the runner chamber wall and the RMS of the vertical vibration component IMF3 and the horizontal vibration components IMF2 and IMF4 are linearly negatively correlated with the cavitation coefficient. The associated component between cavitation-induced vibration and pressure pulsation signal is 16 fn and its harmonics. In the process of cavitation, pressure pulsation plays a leading role in vibration.

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Denoising method based on VMD-PCC in φ-OTDR system

Cited by 12 publications

References 16 publications

Distributed φ-OTDR signal classification based on VMD and SVM

Distributed φ-OTDR signal classification based on VMD and SVM

Detection of incipient rotor unbalance fault based on the RIME-VMD and modified-WKN

Correlation analysis of cavitation-induced pressure pulsation and vibration in a bulb turbine

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