Pipeline transport facilities are laid alongside communication optical cable trenches, allowing Distributed fiber acoustic sensing (DAS) systems to utilize the redundant cores in the cable as sensing units, thereby providing a low-cost and safe monitoring solution for pipeline. However, due to the complexity and diversity of the application environment, the system faces limitations in computing power and recognition accuracy in actual use. Achieving high-precision identification and location of intrusion events in massive data and prompting maintenance personnel to take targeted measures in a timely manner can reduce the work pressure of maintenance personnel and have important implications for pipeline safety monitoring. Herein, we propose a multi-class intrusion event detection algorithm based on central point feature, which can achieve high-precision detection of the location, time, and category of detected pipeline perimeter intrusion events by DAS with a minimal model structure and low computing power.
To address the issue of massive data in distributed acoustic sensing system, we proposed using the ultra-low sampling resolution and undersampling techniques to reduce the data volume last year. However, the fading noise of the 1-bit undersampled signal becomes difficult to be completely removed with conventional denoising methods. In this paper, we proposed an integrated fading suppression algorithm to solve this problem. Experimental results show that the proposed approach can completely eliminate the fading noise of the 1-bit undersampled signal, while the vibration signal-to-noise ratio is also enhanced by 7.72 dB.
In this letter, a new method for liquid level measurement based on in-line Mach Zehnder interferometer (MZI) in fiber ring laser cavity is proposed and experimentally demonstrated. Two cascaded peanut-shaped fiber structures are designed to implement MZI with a length of 25 mm. At the beginning, the broadband light source was used to demonstrate the possibility of MZI as a liquid level sensor. The experimental results show that the liquid level sensitivity of -1.057 nm/cm can be achieved in the detection range of 0-20 mm. However, using broadband light sources to build a sensing system has a large 3-dB spectral bandwidth and low signal-to-noise ratio in the available wavelength range. Therefore, these sensing systems may have poor resolution, low accuracy monitoring and limited detection distance. Therefore, the experiment further uses the fiber ring laser (FRL) cavity to replace the broadband light source to realize the liquid level monitoring. Thanks to narrow laser linewidth and high signal-to-noise ratio, higher detection accuracy can be obtained. MZI acts as a filter and a sensor at the same time. When the liquid level changes, the interference peak shift plays the role of filtering and wavelength selection. The results showed that the detection sensitivity was as high as -1.348 nm/cm. Accompanied by a signal-to-noise ratio of up to 50 dB and a 3-dB linewidth of less than 0.2 nm. Besides, the fiber ring laser cavity can theoretically extend the length of single-mode fiber infinitely in the cavity. Hence, the sensor designed is exemplary and representative for liquid level monitoring in unconventional areas in the far field such as: extreme geological landforms, high temperature and high-pressure regions and anaerobic areas.
The combination of phase-sensitive optical time-domain reflectometry (Φ-OTDR) and deep learning algorithms is a popular research topic in recent years. To train the classification models established by these algorithms, a huge amount of data is required. Though Φ-OTDR can monitor the applied perturbations along the sensing fiber continuously, the huge volume of data puts a heavy burden on storage devices. In this paper, we propose a lossy data compression method based on quantization technology to solve the data storage problem in heterodyne Φ-OTDR. Experimental results show that the vibration waveform can be successfully restored from the reconstructed signal. A compression ratio of 16 is achieved by quantifying a 128-MB data to 31.25 MB with 293.2-ms time consumption. With the proposed quantization method, it is expected to be able to store more sensing data without making modifications to the hardware.
This paper proposes a disturbance recognition method for phase-sensitive optical time-domain reflectometry (Φ-OTDR) based on Faster-RCNN. The method achieves high-speed detection of intrusion location and classification with high accuracy. Our scheme makes full use of the 2D sensing information on spatial-temporal images and uses the advanced "two-step" object detection algorithm Faster-RCNN to achieve real-time operation. Firstly, to improve the detection speed, Region Proposal Network (RPN) and Region of Interest (RoI) are used. Secondly, our CNN-based approach can extract features automatically of disturbance events from spatial-temporal images. So, it has better robustness compared to traditional machine learning methods. Thirdly, the method uses an end-to-end CNN object detection model that integrates multiple modules into a single network. Therefore, it has a significant advantage in detection speed. We conducted data collection under perimeter security scenarios and acquired 4 types of events with a total of 4987 samples. The four events contain “rigid collision”, “hitting net”, “shaking net”, and “cutting net”, which are representative in the perimeter security scenario. Experimental results proves that our method can achieve a real-time operation (0.1659 s processing time for 0.5 s sensing data) with high accuracy (96.32%), shows great potential in real-time disturbance detection for online monitoring industrial application of Φ-OTDR.
Curvature measurement is important in the fields of structure health monitoring, robot-pose measurement, etc. The curvature-sensor with high resolution is highly needed. In this paper, an optical curvature sensor with high resolution based on single-mode-multimode-single-mode (SMS) structure using ring core fiber (RCF) is proposed and its demodulation is realized by the microwave photonic filter (MPF). The SMS fiber structure is a typical in-fiber Mach-Zehnder interferometer (MZI). The operating principle of the proposed sensor is that different curvatures will cause the variations of the interference spectrum of the SMS-sensor, and then the variations are converted into the frequency-shift of the MPF. Theoretical analysis and experiment are verified. The measured sensitivity is -147.634 MHz/m-1, and the resolution is 6.774 × 10-6m-1 which is the highest value up to now.
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