Abstract: Remote terminal field device monitoring using distributed fiber optic sensing (DFOS) comprising a length of optical sensor fiber extending into the remote terminal, said remote terminal including one or more field devices located therein, wherein said length of optical sensor fiber located in the remote terminal includes one or more fiber loops formed in the length of optical sensor fiber, said one or more fiber loops respectively positioned proximate to the one or more field device(s) located in the remote terminal, a DFOS interrogator in optical communication with the optical sensor fiber and configured to generate optical pulses, introduce the generated pulses into the length of optical sensor fiber, and receive backscattered signals from the one or more fiber loops formed in the length of the optical sensor fiber, and an intelligent analyzer configured to analyze DFOS data received by the DFOS interrogator and determine from the backscattered signals, environmental activity occurring at the one or more fi

Abstract: Aspects of the present disclosure describe dynamic road traffic noise mapping using DFOS over a telecommunications network that enables mapping of road traffic-induced noise at any observer location. DFOS is used to obtain instant traffic data including vehicle speed, volume, and vehicle types, based on vibration and acoustic signal along the length of a sensing fiber along with location information. A sound pressure level at a point of interest is determined, and traffic data associated with such point is incorporated into a reference noise emission database and a wave propagation theory for total sound pressure level prediction and mapping. Real-time wind speed using DFOS—such as distributed acoustic sensing (DAS)—is obtained to provide sound pressure adjustment due to the wind speed.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously sense/monitor outdoor facilities and structures including outdoor cabinets containing fiber optic facilities in which the cabinet/fiber optic cable contained therein are configured to provide superior acoustic sensing. Further outdoor facilities and structures that are monitored include manhole structures. Superior DFOS/DAS monitoring results are obtained by employing a machine learning-based analysis method that employs a temporal relation network (TRN).

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing/distributed acoustic sensing (DFOS/DAS) systems, methods, and structures that advantageously provide rainfall intensity measurements along an entire length of a fiber optic sensor. using existing telecommunications optical fiber—which may be part of a multi-fiber, fiber optic cable—that may simultaneously carry live telecommunications traffic. The DFOS/DAS fiber optic sensing is used to obtain vibration and/or sound data from which rainfall intensity measurements may be made along the entire length of the DFOS/DAS fiber optic sensor.

Abstract: Aspects of the present disclosure describe snow / water level detection using distributed fiber optic sensing / distributed acoustic sensing (DFOS/DAS) and an integrated ultrasonic device that advantageously operates over existing optical telecommunications facilities carrying live telecommunications traffic - or optical facilities deployed specifically for such detection. DFOS/DAS monitoring of snow / water level advantageously monitors large areas with high sensitivity while exhibiting robustness to changing environmental conditions and employs a remote (utility pole or other mounting) mounted ultrasonic sensor/transducer that provides snow / water level data in real-time as a coded vibrational signal.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS)—distributed acoustic sensing (DAS) based systems, methods, and structures that advantageously enable and/or facilitate the determination of natural frequency(ies) of civil infrastructures.

Abstract: Systems, and methods for monitoring galloping of overhead transmission lines using distributed fiber optic sensing (DFOS) in combination with frequency domain decomposition (FDD) (frequency domain) algorithms/methods. A DFOS interrogator/analyzer is used to collect real-time data for pre-processing. The pre-processed data is further processed by processing algorithms, which provide results to a graphical user interface or other reporting mechanisms that provide real-time monitoring, alarming, and reporting of the galloping status of the overhead transmission lines.

Abstract: Systems, and methods for automatically determining false transformer humming when using DFOS systems and methods to determine such humming along with machine learning approach(es) to identify the false transformer humming signal(s) that are transferred to a utility pole without a transformer from a working transformer on another utility pole. Advantageously, our inventive systems and methods employ a customized signal processing workflow to process raw data collected from the DFOS. Our employs a binary classifier that can automatically identify a transformer humming signal from a utility pole with a transformer and simultaneously identify the false humming signal from a utility pole without a transformer.

Abstract: Systems and methods for determining fiber optic facility (cable) location using distributed fiber optic sensing (DFOS) and sequence pattern matching of vibration excitation signals applied to a sensor fiber. The use of sequence pattern matching with unique pattern codes allow for the precise determination of location and length of deployed fiber cable while exhibiting an immunity from environmental vibrations proximate to the fiber. As a result improved measurements are realized and false alarms are eliminated.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS)-distributed acoustic sensing (DAS) based systems, methods, and structures that advantageously enable and/or facilitate the monitoring of civil infrastructures via acoustic wave speed measurements.

Abstract: A testing procedure including a data collection procedure and a contrastive learning-based approach, for establishing a profile for utility poles surveyed in an embedding space. Unique properties of utility poles are preserved in a low-dimensional feature vector. Similarities between pairs of samples collected at the same or different poles is reflected by the Euclidean distance between the pole embeddings. During data collection—variabilities of excitation signals are manually introduced, e.g. impact strength, impact locations, impact time ambiguity, data collecting location ambiguity on a DFOS/DAS optical sensor fiber/cable. Data so collected provides a learned model learned complete information about a utility pole and is more robust with respect to uncontrollable factors during operation. A model training procedure that effectively extracts a utility pole intrinsic properties (e.g., structure integrity, dimensions, structure variety) and remote extrinsic influence (e.g.

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: Systems and methods for reducing work conflicts is provided. The method includes receiving a vibrational signal from a utility pole; identifying a location and type of field work on the utility pole from one or more features of the vibrational signal utilizing a trained neural network; and communicating the location and type of field work to a third party.

Abstract: Distributed fiber optic sensing (DFOS) and artificial intelligence (AI) systems and methods for performing utility pole hazardous event localization that advantageously identify a utility pole that has undergone a hazardous event such as being struck by an automobile or other detectable impact. Systems and methods according to aspects of the present disclosure employ machine learning methodologies to uniquely identify an affected utility pole from a plurality of poles. Our systems and methods collect data using DFOS techniques in telecommunication fiber optic cable and use an AI engine to analyze the data collected for the event identification. The AI engine recognizes different vibration patterns when an event happens and advantageously localizes the event to a specific pole and location on the pole with high accuracy. The AI engine enables analyses of events in real-time with greater than 90% accuracy.

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 system and method to assess utility pole integrity, by using existing telecom fiber optic cable as a sensor cable, instant mechanical impact on the pole(s), DAS technology and a machine learning model. An instant mechanical impact creates a vibration event on the optical fiber cable mounted/suspended on a target pole, which is detected/recorded by DAS. By applying a machine learning model on the DAS signals, the target pole's integrity condition is obtained.

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: An advance in the art is made according to aspects of the present disclosure directed to the detection and localization of a substantially static weight situated on aerial telecommunications fiber optic cable through the effect of phase-distributed acoustic sensing (?-DAS) and signal analysis of ambient data. In sharp contrast to the prior art, our inventive method does not require a special optical fiber arrangement or type of fiber nor is it susceptible to range limitations that plague the prior art.

Abstract: Aspects of the present disclosure describe the localization of a utility pole by distributed fiber sensing of aerial fiber cable suspended from the utility pole.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously enable city-scale acoustic impulse detection and localization using standard, live aerial telecommunications optical fiber cables through the use of distributed acoustic sensing exhibiting an error of less than 1.22 m.