Abstract: A data-driven street flood warning system that employs distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) and machine learning (ML) technologies and techniques to provide a prediction of street flood status along a telecommunications fiber optic cable route using the DFOS/DAS data and ML models. Operationally, a DFOS/DAS interrogator collects and transmits vibrational data resulting from rain events while an online web server provides a user interface for end-users. Two machine learning models are built respectively for rain intensity prediction and flood level prediction. The machine learning models serve as predictive models for rain intensity and flood levels based on data provided to them, which includes rain intensity, rain duration, and historical data on flood levels.

Abstract: Systems, methods, and structures providing dynamic line rating (DLR) for overhead transmission lines based on distributed fiber optic sensing (DFOS)/distributed temperature sensing (DTS) to determine temperature of the electrical conductors. Environmental conditions such as wind speed, wind direction, and solar radiation data, are collected from environmental sensors and an acoustic modem that convert the digital data collected from the environmental sensors into generated vibration patterns that are subsequently used to vibrationally excite a DFOS optical sensor fiber. The DFOS system monitors the optical sensor fiber and detects, measures, and decodes the vibrational excitations. An Artificial Neural Network (ANN) determines a heat transfer correlation between the temperature of the optical sensor fiber and electrical conductor(s) (core temperature).

Abstract: A solar powered, self-contained DFOS extender that includes a wide-angle camera and a microphone. The DFOS extender monitors the perimeter constantly or at predetermined time intervals both visually and acoustically. It analyses these signals and if an alarming event is detected (such as an animal, a human, a car, or a truck seen or heard), then the DFOS extender generates a coded vibration via its in-built acoustic modem. These coded vibrations are detected by the fiber and an event log is generated. Consequently, a trespasser or false alarms (animals) are detected before they are within detectable distance of the underground DFOS optical sensor fiber. Advantageously, our DFOS extender can be placed in critical locations along a border where an increased detection range is desired and may later be relocated/reinstalled at other locations as well.

Abstract: Systems and methods for utility pole localization employing a DFOS/DAS interrogator located at one end of an optical sensor fiber remotely capture dynamic strains on the optical sensor fiber induced by acoustic events. A captured two-dimensional spatiotemporal map in an ambient noisy environment is analyzed by a trained machine learning model which then automatically detects an area in which a pole is located without requiring domain knowledge. Original DFOS/DAS signals are separated into pole regions and non-pole region time series for machine learning model training. A contrastive loss function measures similarities between low-frequency and high-frequency features. A Gaussian distribution is applied to the original signals to generate weighted labels to eliminate effects of label noise. The machine learning model fuses low-frequency and high-frequency features in the frequency domain for pole region classification.

Abstract: In sharp contrast to the prior art, a fallen tree detection and localization method based on distributed fiber optical sensing (DFOS) technique and physics informed machine learning is described in which DFOS leverages existing fiber cables that are conventionally installed on the bottom layer of distribution lines and used to provide high-speed communications. The DFOS collects and transmits fallen tree induced vibration data along the length of the entire overhead lines, including distribution lines and transmission lines, where there is a fiber cable deployed. The developed physics-informed neural network model processes the data and localizes the fallen tree location along the lines. The location is interpreted in at least two aspects: the fallen tree location in terms of the fiber cable length; and the exact cable location (power cable or fiber cable) that the fallen tree mechanically impacts.

Abstract: Systems and methods for performing the dynamic anomaly localization of utility pole aerial/suspended/supported wires/cables by distributed fiber optic sensing. In sharp contrast to the prior art, our inventive systems and methods according to aspects of the present disclosure advantageously identify a “location region” on a utility pole supporting an affected wire/cable, thereby permitting the identification and reporting of service personnel that are uniquely responsible for responding to such anomalous condition(s).

Abstract: Systems and methods for performing the dynamic anomaly localization of utility pole aerial/suspended/supported wires/cables by distributed fiber optic sensing. In sharp contrast to the prior art, our inventive systems and methods according to aspects of the present disclosure advantageously identify a “location region” on a utility pole supporting an affected wire/cable, thereby permitting the identification and reporting of service personnel that are uniquely responsible for responding to such anomalous condition(s).

Abstract: A method of utility pole integrity assessment by distributed fiber optic sensing/distributed acoustic sensing (DFOS/DAS) employing existing telecommunications fiber optic cable as a sensor. The fiber optic cable is suspended aerially from a plurality of utility poles and a machine learning model is developed during training by mechanically exciting the utility poles. Once developed, and in sharp contrast to the prior art, the machine learning model—in conjunction with DFOS/DAS operation—determines an integrity assessment for a plurality of the utility poles aerially suspending the fiber optic cable from a mechanical impact of a single pole.

Abstract: Computer vision based, wide-area snow/water level estimation methods using disparity maps. In one embodiment our method provides rich depth-information using a stereo camera and image processing. Scene images at normal and snow/rain weather conditions are obtained by a double-lens stereo camera and a disparity map is generated from the scene images at left and right lenses using a self-supervised deep convolutional network. In another embodiment, our method uses a single point snow/water level sensor, a stationary monocular camera to measure snow/water levels covering a wide area.

Abstract: Disclosed are systems and methods to determine barometric pressure 1) using multiple low-cost pressure sensors located at known heights instead of a single high-cost sensor; 2) determines an actual pressure value—not by averaging multiple sensors but rather optimizing an expected error in each individual one of them and utilize their known sensor heights thereby defining a new error function; 3) our approach is scalable, i.e. the number of sensors can be increased, and multiple sensors can be grouped together into smaller cells such that each group of cell can be corrected separately, and can even be corrected among themselves. Finally, our systems and methods according to the present disclosure can advantageously be integrated with a distributed fiber optic sensing (DFOS) system via acoustic modems thereby providing extremely wide-area external, or interior buildings pressure readings.

Abstract: A radio-controlled, two-way acoustic modem for operating with a distributed fiber optic sensing (DFOS) system including circuitry that receives radio signals including configuration information, configures the modem to operate according to the configuration information, and generate acoustic signals that are detected by the DFOS system. The acoustic modem includes one or more sensors that detect environmental information that is encoded in the acoustic signals for further reception by the DFOS system. The received configuration information may change the operating times, sensors or other operating aspects of the modem as desired an such information may be transmitted from a fixed location or a mobile vehicle.

Abstract: A distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) system and method employing a fiber optic sensor cable that collects vibrational data of individual utility poles suspending the fiber optic sensor cable and stores the vibrational data in a central office (CO). Machine learning (ML) models are developed, trained, and utilized to analyze vibrational features of the utility poles and determine their integrity. Additionally, DFOS/DAS systems and methods according to the present disclosure determine the location(s) of fiber coils that exist along a length of a fiber optic sensor cable.

Abstract: A distributed fiber optic sensing (DFOS) / distributed acoustic sensing (DAS) system and method employing a fiber optic sensor cable that collects vibrational data of individual utility poles suspending the fiber optic sensor cable and stores the vibrational data in a central office (CO). Machine learning (ML) models are developed, trained, and utilized to analyze vibrational features of the utility poles and determine their integrity. Additionally, DFOS / DAS systems and methods according to the present disclosure determine the location(s) of fiber coils that exist along a length of a fiber optic sensor cable.

Abstract: An acoustic modem for operating with a distributed fiber optic sensing (DFOS) system that generates acoustic signals that are detected by the DFOS system. The acoustic modem is powered by energy harvesters includes one or more sensors that detect environmental information that is encoded in the acoustic signals for further reception by the DFOS system. The acoustic modem may include several vibrator elements that provide a vibration signals imparted on the DFOS system fiber sensor.

Abstract: A radio-controlled, two-way acoustic modem for operating with a distributed fiber optic sensing (DFOS) system including circuitry that receives radio signals including configuration information, configures the modem to operate according to the configuration information, and generate acoustic signals that are detected by the DFOS system. The acoustic modem includes one or more sensors that detect environmental information that is encoded in the acoustic signals for further reception by the DFOS system. The received configuration information may change the operating times, sensors or other operating aspects of the modem as desired and such information may be transmitted from a fixed location or a mobile vehicle.

Abstract: Distributed fiber optic sensing (DFOS) systems and methods for monitoring electrical power distribution transformers and locating failing/failed/malfunctioning transformers from humming signals indicative of a transformer's operational integrity and are advantageously detected/analyzed via DFOS mechanisms.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) systems, methods, and structures that employ machine learning and provide for the automatic remote inspection and condition evaluation of wooden utility poles. Operationally, audio (acoustic) signals are obtained using DFOS/DAS when a service technician/inspector strikes the wooden utility poles with an impact tool such as a hammer. Historical audio DFOS/DAS signals that include signals resulting from hollow (decayed) utility poles and solid (good) poles are used to train one or more machine learning models and the trained machine learning models are subsequently used to evaluate real-time impact data collected from DFOS/DAS and determine utility pole condition in real-time.

Abstract: Distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) techniques coupled with frequency domain decomposition (FDD) are employed to determine the presence of 120 HZ operating power frequency to determine when street lights are energized and on thereby providing continuous status monitoring of the operational status of street lights and locating affected street lights at a particular utility pole location.

Abstract: Systems and methods for operating a distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) system include a length of optical sensing fiber suspended aerially by a plurality of utility poles and in optical communication with a DFOS interrogator/analyzer. The method includes operating the DFOS/DAS system while manually exciting more than one of the poles to obtain frequency response(s) of the excited poles; contrastively training a convolutional neural network (CNN) with the frequency responses obtained; classifying the utility poles using the contrastively trained CNN; and generating a profile map of the excited poles indicative of the classified utility poles.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously enable and/or facilitate the continuous monitoring and identification of damaged utility poles by employing a DFOS distributed acoustic sensing (DAS) methodology in conjunction with a finite element analysis and operational modal analysis. Of particular advantage and in further contrast to the prior art, systems, methods, and structures according to aspects of the present disclosure utilize existing optical fiber supported/suspended by the utility poles as a sensing medium for the DFOS/DAS operation.