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.

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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.

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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.

This paper presents an on-line augmented surveillance system that aims to real time monitoring of activities along a pipeline. The system is deployed in a fully realistic scenario and exposed to real activities carried out in unknown places at unknown times within a given test time interval (socalled blind field tests). We describe the system architecture that includes specific modules to deal with the fact that continuous on-line monitoring needs to be carried out, while addressing the need of limiting the false alarms at reasonable rates. To the best or our knowledge, this is the first published work in which a pipeline integrity threat detection system is deployed in a realistic scenario (using a fiber optic along an active gas pipeline) and is thoroughly and objectively evaluated in realistic blind conditions. The system integrates two operation modes: The machine+activity identification mode identifies the machine that is carrying out a certain activity along the pipeline, and the threat detection mode directly identifies if the activity along the pipeline is a threat or not. The blind field tests are carried out in two different pipeline sections: The first section corresponds to the case where the sensor is close to the sensed area, while the second one places the sensed area about 35 km far from the sensor. Results of the machine+activity identification mode showed an average machine+activity classification rate of 46.6%. For the threat detection mode, 8 out of 10 threats were correctly detected, with only 1 false alarm appearing in a 55.5-hour sensed period.

Abstract-The use of linearly chirped probe pulses in phase sensitive-(Φ)OTDR technology has been recently demonstrated to allow for high-resolution, quantitative and dynamic temperature or strain variation measurements in a simple and very robust manner. This new sensing technology, known as chirped-pulse ΦOTDR, had a maximum reported sensing range of 11 km. In this paper, a 75 km sensing range with 10 m spatial resolution is demonstrated by using bidirectional first order Raman amplification. The system is capable of performing truly linear, single-shot measurements of strain perturbations with an update rate of 1 kHz and 1 nε resolution. The time-domain trace of the sensor exhibits a signal to noise ratio (SNR) in the worst point of >3 dB, allowing to monitor vibrations up to 500 Hz with remarkable accuracy. To demonstrate the capabilities of the proposed system, we apply <100 nε vibrations in the noisiest point of the fiber, with a frequency modulated from 70 Hz to 150 Hz over a period of 10 s. The results obtained in these conditions demonstrate a vibration detection SNR of >20 dB (with only 300 ms analysis window and no post-processing) and no evidence of nonlinearity in the acoustic response. The optical nonlinear effects that the probe pulse could suffer along the sensing fiber are thoroughly studied, paying special attention to potential distortions of the pulse shape, particularly in its instantaneous frequency profile. Our analysis reveals that, for proper values of peak power, the pulse does not suffer any major distortion and therefore the system performance is not compromised.

Phase-sensitive optical time-domain reflectometry (φOTDR) is widely used for the distributed detection of mechanical or environmental variations with resolutions of typically a few meters. The spatial resolution of these distributed sensors is related to the temporal width of the input probe pulses. However, the input pulse width cannot be arbitrarily reduced (to improve the resolution), since a minimum pulse energy is required to achieve a good level of signal-to-noise ratio (SNR), and the pulse peak power is limited by the advent of nonlinear effects. In this Letter, inspired by chirped pulse amplification concepts, we present a novel technique that allows us to increase the SNR by several orders of magnitude in φOTDR-based sensors while reaching spatial resolutions in the centimeter range. In particular, we report an SNR increase of 20 dB over the traditional architecture, which is able to detect strain events with a spatial resolution of 1.8 cm.