So far, the optical pulses used in phase-sensitive OTDR (ΦOTDR) were typically engineered so as to have a constant phase along the pulse. In this work, it is demonstrated that by acting on the phase profile of the optical pulses, it is possible to introduce important conceptual and practical changes to the traditional ΦOTDR operation, thus opening a door for new possibilities which are yet to be explored. Using a ΦOTDR with linearly chirped pulses and direct detection, the distributed measurement of temperature/strain changes from trace to trace, with 1mK/4nε resolution, is theoreticaly and experimentaly demonstrated. The measurand resolution and sensitivity can be tuned by acting on the pulse chirp profile. The technique does not require a frequency sweep, thus greatly decreasing the measurement time and complexity of the system, while maintaining the potential for metric spatial resolutions over tens of kilometers as in conventional ΦOTDR. The technique allows for measurements at kHz rates, while maintaining reliability over several hours.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper presents a novel surveillance system aimed at the detection and classification of threats in the vicinity of a long gas pipeline. The sensing system is based on phase-sensitive optical time domain reflectometry (φ-OTDR) technology for signal acquisition and pattern recognition strategies for threat identification. The proposal incorporates contextual information at the feature level and applies a system combination strategy for pattern classification. The contextual information at the feature level is based on the tandem approach (using feature representations produced by discriminatively-trained multi-layer perceptrons) by employing feature vectors that spread different temporal contexts. The system combination strategy is based on a posterior combination of likelihoods computed from different pattern classification processes. The system operates in two different modes:(1) machine + activity identification, which recognizes the activity being carried out by a certain machine, and (2) threat detection, aimed at detecting threats no matter what the real activity being conducted is. In comparison with a previous system based on the same rigorous experimental setup, the results show that the system combination from the contextual feature information improves the results for each individual class in both operational modes, as well as the overall classification accuracy, with statistically-significant improvements.

Abstract:There is an increasing interest in researchers and companies on the combination of Distributed Acoustic Sensing (DAS) and a Pattern Recognition System (PRS) to detect and classify potentially dangerous events that occur in areas above fiber optic cables deployed along active pipelines, aiming to construct pipeline surveillance systems. This paper presents a review of the literature in what respect to machine learning techniques applied to pipeline surveillance systems based on DAS+PRS (although its scope can also be extended to any other environment in which DAS+PRS strategies are to be used). To do so, we describe the fundamentals of the machine learning approaches when applied to DAS systems, and also do a detailed literature review of the main contributions on this topic. Additionally, this paper addresses the most common issues related to real field deployment and evaluation of DAS+PRS for pipeline threat monitoring, and intends to provide useful insights and recommendations in what respect to the design of such systems. The literature review concludes that a real field deployment of a PRS based on DAS technology is still a challenging area of research, far from being fully solved.

No abstract