Hybrid Feature Extraction of Pipeline Microstates Based on Φ-OTDR Sensing System

Linear infrastructures, such as railways, tunnels, and pipelines, play essential roles in economic and social development worldwide. However, under the influence of geohazards, earthquakes, and human activities, linear infrastructures face the potential risk of damage and may not function properly. Current monitoring systems for linear infrastructures are mainly based on non-contact detection (InSAR, UAV, GNSS, etc.) and geotechnical instrumentation (extensometers, inclinometers, tiltmeters, piezometers, etc.) techniques. Regarding monitoring sensitivity, frequency, and coverage, most of these methods have some shortcomings, which make it difficult to perform the accurate, real-time, and comprehensive monitoring of linear infrastructures. Distributed acoustic sensing (DAS) is an emerging sensing technology that has rapidly developed in recent years. Due to its unique advantages in long-distance, high-density, and real-time monitoring, DAS arrays have shown broad application prospects in many fields, such as oil and gas exploration, seismic observation, and subsurface imaging. In the field of linear infrastructure monitoring, DAS has gradually attracted the attention of researchers and practitioners. In this paper, recent research and the development activities of applying DAS to monitor different types of linear infrastructures are critically reviewed. The sensing principles are briefly introduced, as well as the main features. This is followed by a summary of recent case studies and some critical problems associated with the implementation of DAS monitoring systems in the field. Finally, the challenges and future trends of this research area are presented.

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“…In recent years, many successful trials have been performed [ 87 , 88 , 89 , 90 ]. In 2015, Wu et al applied DAS to pipeline leakage monitoring.…”

Section: Applications Of Das In Linear Infrastructure Monitoringmentioning

confidence: 99%

Distributed Acoustic Sensing for Monitoring Linear Infrastructures: Current Status and Trends

Zhu

Liu

Wang

et al. 2022

Sensors

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“…The ϕ-OTDR system offers advantages such as high sensitivity [1], high resolution [2], and a simple structure [3]. It has been widely applied in various fields including pipeline monitoring [4,5], power communication networks [6], and perimeter security [7,8]. In recent years, there have been significant improvements in the performance of ϕ-OTDR, such as increased sensing fiber length [9] and improved spatial resolution [10,11].…”

Section: Introductionmentioning

confidence: 99%

Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite Net

Chen,

Yang,

et al. 2023

Electronics

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The phase-sensitive optical time-domain reflectometer (φ-OTDR) is commonly used in various industries such as oil and gas pipelines, power communication networks, safety maintenance, and perimeter security. However, one challenge faced by the φ-OTDR system is low pattern recognition accuracy. To overcome this issue, a Dendrite Net (DD)-based pattern recognition method is proposed to differentiate the vibration signals detected by the φ-OTDR system, and normalize the differential signals with the original signals for feature extraction. These features serve as input for the pattern recognition task. To optimize the DD for the pattern recognition of the feature vectors, the Variable Three-Term Conjugate Gradient (VTTCG) is employed. The experimental results demonstrate the effectiveness of the proposed method. The classification accuracy achieved using this method is 98.6%, which represents a significant improvement compared to other techniques. Specifically, the proposed method outperforms the DD, Support Vector Machine (SVM), and Extreme Learning Machine (ELM) by 7.5%, 8.6%, and 1.5% respectively. The findings of this research paper indicate that the pattern recognition method based on DD and optimized using the VTTCG can greatly enhance the accuracy of the φ-OTDR system. This improvement has important implications for various applications in industries such as pipeline monitoring, power communication networks, safety maintenance, and perimeter security.

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Unsupervised learning method for events identification in φ-OTDR

Zhang

Zhao

et al. 2022

Opt Quant Electron

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In this paper, an unsupervised-learning method for events-identi cation in φ-OTDR ber-optic distributed vibration sensor is proposed. The different vibration-events including blowing, raining, direct and indirect hitting, and noise-induced false vibration are clustered by the k-means algorithm. The equivalent classi cation accuracy of 99.4% has been obtained, compared with the actual classes of vibration-events in the experiment. With the cluster-number of 3, the maximal Calinski-Harabaz index and Silhouette coe cient are obtained as 2653 and 0.7206, respectively. It is found that our clustering method is effective for the events-identi cation of φ-OTDR without any prior labels, which provides an interesting application of unsupervised-learning in self-classi cation of vibration-events for φ-OTDR.

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Hybrid Feature Extraction of Pipeline Microstates Based on Φ-OTDR Sensing System

Cited by 9 publications

References 25 publications

Distributed Acoustic Sensing for Monitoring Linear Infrastructures: Current Status and Trends

Distributed Acoustic Sensing for Monitoring Linear Infrastructures: Current Status and Trends

Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite Net

Unsupervised learning method for events identification in φ-OTDR

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