Study on the algorithm of vibration source identification based on the optical fiber vibration pre-warning system

Qu, Hongquan; Ren, Xuecong; Li, Guoxiang; Li, Yonghong; Zhang, Changnian

doi:10.1007/s13320-015-0245-0

Cited by 16 publications

(6 citation statements)

References 5 publications

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“…In 2014, Q. Li et al carried out power spectrum analysis and used the total energy of each sampling point as a feature to locate the intrusion 142 . In 2015, H. Qu et al used energy information entropy as the feature to identify raindrop vibration source, and the fundamental frequency as the feature to identify construction machine and train or car passing by 167 . In the same year, C. Cao et al selected the total energy, the ratio of the low-band energy to the total energy, and the ratio of the peak amplitude to the average value of the spectrum as three features.…”

Section: Event Discriminationmentioning

confidence: 99%

Advances in phase-sensitive optical time-domain reflectometry

Liu¹,

Yu²,

Hong³

et al. 2022

OEA

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Phase-sensitive optical time-domain reflectometry (Φ-OTDR) has attracted numerous attention due to its superior performance in detecting the weak perturbations along the fiber. Relying on the ultra-sensitivity of light phase to the tiny deformation of optical fiber, Φ-OTDR has been treated as a powerful technique with a wide range of applications. It is fundamental to extract the phase of scattering light wave accurately and the methods include coherent detection, I/Q demodulation, 3 by 3 coupler, dual probe pulses, and so on. Meanwhile, researchers have also made great efforts to improve the performance of Φ -OTDR. The frequency response range, the measurement accuracy, the sensing distance, the spatial resolution, and the accuracy of event discrimination, all have been enhanced by various techniques. Furthermore, lots of researches on the applications in various kinds of fields have been carried out, where certain modifications and techniques have been developed. Therefore, Φ-OTDR remains as a booming technique in both researches and applications.

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Section: Event Discriminationmentioning

confidence: 99%

Advances in phase-sensitive optical time-domain reflectometry

Liu¹,

Yu²,

Hong³

et al. 2022

OEA

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“…H. Zhu et al set an empirical value as threshold and calculate the rate of measured trace crossing through the threshold as a feature, namely the level crossing rate [44]. In [45], Qu et al use the local maximum amplitude and short-time average energy as the feature. F. Jiang et al use spatial kurtosis as a time-domain feature, which denotes the rapid change of the measured light intensity over time [46].…”

Section: A Feature Extractionmentioning

confidence: 99%

Data-Driven Distributed Optical Vibration Sensors: A Review

et al. 2020

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Distributed optical vibration sensors (DOVS)have attracted much attention recently since it can be used to monitor mechanical vibrations or acoustic waves with long reach and high sensitivity. Phase-sensitive optical time domain reflectometry ( -OTDR) is one of the most commonly used DOVS schemes. For -OTDR, the whole length of fiber under test (FUT) works as the sensing instrument and continuously generates sensing data during measurement. Researchers have made great efforts to try to extract external intrusions from the redundant data. High signal-to-noise ratio (SNR) is necessary in order to accurately locate and identify external intrusions in -OTDR systems. Improvement in SNR is normally limited by the properties of light source, photodetector and FUT. But this limitation can also be overcome by post-processing of the received optical signals. In this context, detailed methodologies of SNR enhancement post-processing algorithms in -OTDR systems have been described in this paper. Furthermore, after successfully locating the external vibrations, it is also important to identify the types of source of the vibrations. Pattern classification is a powerful tool in recognizing the intrusion types from the vibration signals in practical applications. Recent reports of -OTDR systems employed with pattern classification algorithms are subsequently reviewed and discussed. This thorough review will provide a design pathway for improving the performance of -OTDR while maintaining the cost of the system as no additional hardware is required.

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“…It is known that the constant false alarm rate (CFAR) is a common target automatic detection method for radar signal processing [8]. Among them, the cell average constant false alarm rate (CA-CFAR) method is widely used in optical fiber vibration signals processing with the advantages of strong adaptability and low computation cost [9][10][11][12]. The CA-CFAR detector achieves the best performance when the background units are IID, and its performance is close to the detection performance of the Neyman-Pearson detector [13].…”

Section: Introductionmentioning

confidence: 99%

Study on CA-CFAR Algorithm Based on Normalization Processing of Background Noise for HI of Optical Fiber

Wang

Zhao

et al. 2018

Photonic Sens

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Optical fiber pre-warning system (OFPS) is often used to monitor the occurrence of disasters such as the leakage of oil and natural gas pipeline. It analyzes the collected vibration signals to judge whether there is any harmful intrusion (HI) events. At present, the research in this field is mainly focused on the constant false alarm rate (CFAR) methods and derivative algorithms to detect intrusion signals. However, the performance of CFAR is often limited to the actual collected signals distribution. It is found that the background noise usually obeys non-independent and identically distribution (Non-IID) through the statistical analysis of acquisition signals. In view of the actual signal distribution characteristics, this paper presents a CFAR detection method based on the normalization processing for background noise. A high-pass filter is designed for the actual Non-IID background noise data to obtain the characterization characteristic. Then, the background noise is converted to independent and identically distribution (IID) by using the data characteristic. Next, the collected data after normalization is processed with efficient cell average constant false alarm rate (CA-CFAR) method for detection. Finally, the results of experiments both show that the intrusion signals can be effectively detected, and the effectiveness of the algorithm is verified.

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Study on the algorithm of vibration source identification based on the optical fiber vibration pre-warning system

Cited by 16 publications

References 5 publications

Advances in phase-sensitive optical time-domain reflectometry

Advances in phase-sensitive optical time-domain reflectometry

Data-Driven Distributed Optical Vibration Sensors: A Review

Study on CA-CFAR Algorithm Based on Normalization Processing of Background Noise for HI of Optical Fiber

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