Multiple disturbance detection and intrusion recognition in distributed acoustic sensing

Jianfen Huang; Tuanwei Xu; Shengwen Feng; Yang Yang; Fang Li; Jinming Zhou; Hesper Yu

doi:10.1117/12.2503871

12 December 2018 Multiple disturbance detection and intrusion recognition in distributed acoustic sensing

Jianfen Huang, Tuanwei Xu, Shengwen Feng, Yang Yang, Fang Li, Jinming Zhou, Hesper Yu

Proceedings Volume 10849, Fiber Optic Sensing and Optical Communication; 108490E (2018) https://doi.org/10.1117/12.2503871
Event: International Symposium on Optoelectronic Technology and Application 2018, 2018, Beijing, China

Abstract

Distributed acoustic sensing system can be used in the long-distance and strong-EMI condition for monitoring and inspection. In this paper, location method for optical fiber multiple dynamic disturbances signals is proposed to solve the difficulty with Distributed acoustic sensing (DAS) system in effectively locates multiple dynamic disturbances. The first step: locate multiple dynamic disturbances signals exactly by using the multiple threshold method. The second step: the Empirical Mode Decomposition(EMD) method and the Fourier transform(FFT) is proposed to extract the signal features . By analyzing the time domain signals of the intrusion location that we can look for the most efficient signal feature to form a pattern feature vectors for classification. After the first two steps, we can get feature vectors of different types of dynamic disturbances. By utilizing support vector machine(SVM) classifiers to identify feature vectors, patterns of intrusion events are recognized accurately. Experiments show that after using this method to process 300 dynamic disturbances samples generated by three different intrusion events, namely, passing, hurling and knocking, the location accuracy is about 1.6m, the recognition rates of intrusion events are over 90%.

Citation Download Citation

Jianfen Huang, Tuanwei Xu, Shengwen Feng, Yang Yang, Fang Li, Jinming Zhou, and Hesper Yu "Multiple disturbance detection and intrusion recognition in distributed acoustic sensing", Proc. SPIE 10849, Fiber Optic Sensing and Optical Communication, 108490E (12 December 2018); https://doi.org/10.1117/12.2503871

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KEYWORDS

Signal processing

Acoustics

Optical fibers

Pattern recognition

Data acquisition

Inspection

Feature extraction

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