This application relates to distributed fiber optic sensing (DFOS) systems, methods, and structures. More particularly, it pertains to source localization with joint time arrival and velocity estimation for cable cut prevention using distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS).
To support 5G and beyond networks, there are many optical fibers buried underground that serve a significant role for essential communications. However, many construction activities (especially machine digging) may cut an optical fiber cable and produce a network outage. Hence, it is critical for service providers to protect optical fiber facilities since every fiber cut event will induce down time, raise maintenance cost and potentially impact a service provider's SLA (service level agreement) penalty. Currently, there is no satisfactory solution to prevent optical fiber cable cuts unless one follows the well-known 811 tickets and physically marks out cable locations. Distributed fiber optic sensing (DFOS) techniques provide a continuous monitoring along the entire cable route and detect the construction actions in early stage to prevent the cable cut and protect the fiber.
The above problems are solved and an advance in the art in is made according to aspects of the present disclosure directed to source localization for cable cut prevention using distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS).
In sharp contrast to the prior art, we describe a method to localize construction activities from distributed fiber optic sensing data. Unlike earlier methods that suffer from uncertainties resulting from changing underground propagation speed of sound/vibration that can change significantly with changes in temperature and humidity, our inventive method according to the present disclosure is robust/immune to this underground propagation speed uncertainty.
According to aspects of the present disclosure, if an activity that threatens an underground facility is occurring close to an optical fiber, an alarm is signaled to stop construction activities. As a result, the optical fiber(s) threatened by the proximate construction activities are not damaged.
According to further aspects of the present disclosure, our inventive method estimates the location of a vibration source while considering any uncertainty of vibration propagation speed.
In further contrast to the prior art—which assumed a known propagation speed and are “blind” to environmental changes—our inventive method formulates the localization as an optimization problem, and both location of the sources and the propagation speed are treated as unknown. This advantageously enables our method to adapt to variances of the velocity and produce a better generalized performance with respect to environmental changes experienced in the field.
Our systems and methods include a DFOS system and AI techniques as an integrated solution for vibration source localization along an entire optical sensor fiber cable route. As we shall show and describe further, our inventive systems and methods process real-time DFOS data and extract features that are related to a location of a source of vibrations that may threaten optical fiber facilities. Our processing framework is based on a physical propagation model that incorporates: an uncertainty of the underground propagation velocity wherein we formulate a novel feature space that includes both the velocity and distance. The distance of the vibration source from the optical sensor fiber and underground wave propagation velocity are jointly considered in our optimization framework which simultaneously estimates the distance of the source of the vibrations and the underground wave propagation velocity. Advantageously, our inventive systems and methods provide a model that generalizes environmental changes and unknown sources and is robust to traffic noises and optical sensor fiber noises.
The following merely illustrates the principles of this disclosure. It will thus be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the disclosure and are included within its spirit and scope.
Furthermore, all examples and conditional language recited herein are intended to be only for pedagogical purposes to aid the reader in understanding the principles of the disclosure and the concepts contributed by the inventor(s) to furthering the art and are to be construed as being without limitation to such specifically recited examples and conditions.
Moreover, all statements herein reciting principles, aspects, and embodiments of the disclosure, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure.
Thus, for example, it will be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the disclosure.
Unless otherwise explicitly specified herein, the FIGs comprising the drawing are not drawn to scale.
By way of some additional background, we note that distributed fiber optic sensing systems interconnect opto-electronic integrators to an optical fiber (or cable), converting the fiber to an array of sensors distributed along the length of the fiber. In effect, the fiber becomes a sensor, while the interrogator generates/injects laser light energy into the fiber and senses/detects events along the fiber length.
As those skilled in the art will understand and appreciate, DFOS technology can be deployed to continuously monitor vehicle movement, human traffic, excavating activity, seismic activity, temperatures, structural integrity, liquid and gas leaks, and many other conditions and activities. It is used around the world to monitor power stations, telecom networks, railways, roads, bridges, international borders, critical infrastructure, terrestrial and subsea power and pipelines, and downhole applications in oil, gas, and enhanced geothermal electricity generation. Advantageously, distributed fiber optic sensing is not constrained by line of sight or remote power access and—depending on system configuration—can be deployed in continuous lengths exceeding 30 miles with sensing/detection at every point along its length. As such, cost per sensing point over great distances typically cannot be matched by competing technologies.
Distributed fiber optic sensing measures changes in “backscattering” of light occurring in an optical sensing fiber when the sensing fiber encounters environmental changes including vibration, strain, or temperature change events. As noted, the sensing fiber serves as sensor over its entire length, delivering real time information on physical/environmental surroundings, and fiber integrity/security. Furthermore, distributed fiber optic sensing data pinpoints a precise location of events and conditions occurring at or near the sensing fiber.
A schematic diagram illustrating the generalized arrangement and operation of a distributed fiber optic sensing system that may advantageously include artificial intelligence/machine learning (AI/ML) analysis is shown illustratively in
As is known, contemporary interrogators are systems that generate an input signal to the optical sensing fiber and detects/analyzes reflected/backscattered and subsequently received signal(s). The received signals are analyzed, and an output is generated which is indicative of the environmental conditions encountered along the length of the fiber. The backscattered signal(s) so received may result from reflections in the fiber, such as Raman backscattering, Rayleigh backscattering, and Brillion backscattering.
As will be appreciated, a contemporary DFOS system includes the interrogator that periodically generates optical pulses (or any coded signal) and injects them into an optical sensing fiber. The injected optical pulse signal is conveyed along the length optical fiber.
At locations along the length of the fiber, a small portion of signal is backscattered/reflected and conveyed back to the interrogator wherein it is received. The backscattered/reflected signal carries information the interrogator uses to detect, such as a power level change that indicates—for example—a mechanical vibration.
The received backscattered signal is converted to electrical domain and processed inside the interrogator. Based on the pulse injection time and the time the received signal is detected, the interrogator determines at which location along the length of the optical sensing fiber the received signal is returning from, thus able to sense the activity of each location along the length of the optical sensing fiber. According to aspects of the present disclosure, classification methods may be further used to detect and locate events or other environmental conditions including acoustic and/or vibrational and/or thermal along the length of the optical sensing fiber.
Simulate the Machine Digging Activities
The DFOS system is connected to optical sensing fiber to provide sensing functions in long-term and real-time. The optical sensing fiber can be a dark fiber (no telecommunications traffic) or an operational fiber carrying telecommunications traffic to/from telecommunications service providers. In this disclosure, we are focusing on machine digging activities which as will be known to those skilled in the art, is a considerable threat to the underground optical fiber cable. Due to the limitation of the test equipment, we evaluate our methodology by dropping a 40 LB weight plate to simulate the impulse signals of a machine digging.
Obtain the Raw Phase Space-Time Matrix from the DFOS
As those skilled in the art will understand and appreciate, signals received from DFOS system operation is a complex multi-channel time series. Suppose there are N time samples and M channels, the signal received is a complex matrix with dimension N×M. We extract the phase information of the complex matrix for further processing.
Bandpass Filtering and Phase Unwrap
To remove artifacts from a DC component and compensate for the phase change for intense activities, we apply filtering and unwraping the phase matrix we just obtained. The space-time matrix for an impulse event after filtering and phase unwraping can be visualized as
Time-Frequency Analysis and Spectrogram Computation
A short-time Fourier transform (STFT) is applied to a selected set of channels of the (filtered and unwrapped) space-time matrix.
Energy Time Series for Each Channel
For each channel, we sum the spectrogram across frequencies and obtain the energy time series. The energy time series for channel #132 can be plotted as shown in
In
First Arrival Time Curve
We perform a change point detection technique based on thresholding the absolute value of first-order differences to capture the first arrival time for channels #111-#132 and plot the change points for these channels. The wave propagation model and the first arrival estimation are shown in
TDOA Curve
With the help of the change point plot, we can compute the time difference of arrival (TDOA) for each channel relative to the center channel. For the center channel, the TDOA is supposed to be 0. The TDOA curve can be plotted as shown in
An Ensemble Approach Robust to Outliers
We note that some of the change points may be affected by the noise. Therefore, we can exclude them in the fitting likelihood surface computation part. Instead of designing a dedicated outlier removal approach, we propose a random sampling technique, and only ¾ of the channels will be randomly selected in each round. This can be viewed as an ensemble approach which can provide both point estimates of the unknown parameters and their confidence intervals. We will execute this procedure for 50 rounds and record the estimation results for each round.
TDOA Fitting Likelihood Surface and Parameter Estimation
Based on the center channel and the geometry, and if the underground wave propagation speed is known, we can obtain the theoretical TDOA curve. However, to incorporate the uncertainty of the propagation speed, we need to compute the theoretical TDOA curve for each candidate propagation speed. Then, we compute the difference between the TDOA curve obtained previously with the theoretical TDOA curve under different candidate propagation speeds and form a likelihood surface. The minima in the likelihood surface will be an estimation of the vertical distance and velocity.
Therefore, the estimated vertical distance and underground wave propagation velocity are 1.8 m, and 350 m/s, respectively (as “+” indicates in the figure). The ground-truth vertical distance is 2 m (shown as the horizontal line).
In addition, to examine the effectiveness of the random sampling method, we plot the histogram of the estimated d for the 50 rounds.
Experimental Results
We evaluated our inventive method based on the weight drop signal under the sampling frequency 20 kHz. The data are collected in the test bed shown in
Initially, we examine the mean absolute error (MAE) of the proposed method. The following table lists the MAE for the case when random sampling is applied or not applied. Since we are more concerned with the localization performance of the sources within 5 m, we separately evaluate these testing points. In addition, the performance without random sampling (RS) and with RS are also compared.
From Table 1, the proposed method can achieve an accurate estimation for the source within 5 m and RS can improve the performance. In addition, we examine the alarm detection performance of the method.
From
Finally, we examine the generalization performance of the proposed method. We collect the data for another day which has different weather and ground conditions. Therefore, the propagation speed is different from the previous test. Besides, instead of using weight drop as the exciting impulse, we use a tamper to hit the ground to generate the impulse
From
At this point, while we have presented this disclosure using some specific examples, those skilled in the art will recognize that our teachings are not so limited. Accordingly, this disclosure should only be limited by the scope of the claims attached hereto.
This application claims the benefit of U.S. Provisional Patent Application Ser. No. 63/415,713 filed Oct. 13, 2022, the entire contents of which is incorporated by reference as if set forth at length herein.
Number | Date | Country | |
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63415713 | Oct 2022 | US |