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 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: 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 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: 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.