Abstract: A generative Artificial Intelligence (AI) framework is presented based on a conditional diffusion model for distributed acoustic sensing (DAS) data imputation. The proposed model, named “DiffOptics,” is capable of generating high-quality fiber sensing data by learning the distribution of existing acoustic sensing data and conditioning on an adjacent acoustic sensing signal. DiffOptics is designed to address two critical challenges in abnormal acoustic event detection: (1) DAS data imputation to enhance spatial resolution for more accurate event analysis and reduced data storage, and (2) the generation of synthetic DAS data to improve the performance of machine learning models for recognizing hazardous events.

Abstract: A novel, two-stage Distributed Fiber Optic Sensor (DFOS) placement strategy and method that ensures resilient monitoring of critical infrastructure during electrical power supply failures. This strategy and method combines a heuristic algorithm, PURE (Power Source-aware Route Exploration), with Integer Linear Programming (ILP) optimization. The PURE algorithm explores potential DFOS routes while explicitly considering the dependency of DFOS devices on the electrical power distribution network, ensuring routes electrically powered by the same electrical power feeder are disjoint. Subsequently, the ILP selects the optimal set of DFOS placements to minimize the total number of deployed sensors while meeting critical infrastructure monitoring requirements, such as redundant monitoring for high-importance links. The method enhances the observability of critical links, achieving 100% monitoring coverage for important links during simulated power outages affecting an electric feeder.

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: Disclosed is a distributed acoustic sensing (DAS) placement method that explicitly accounts for power supply limitations. By strategically positioning DAS devices within a network in a manner that optimizes power availability and minimizes risk of monitoring blind spots during power outages, our method enhances overall resilience of a DAS monitoring system. Our method combines a heuristic algorithm, PURE (Power Source-aware Route Exploration), with Integer Linear Programming (ILP) optimization. The PURE algorithm explores all possible fiber routes that satisfy both fiber-side constraints-such as linear, non-branching routes within the operational range and power-side constraints, ensuring that DFOS devices are powered independently. The ILP then selects the optimal set of DFOS units to minimize the total number of sensors while meeting monitoring requirements.

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: 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: Disclosed are integrated DFOS systems and methods that advantageously integrates fiber sensing technology with advanced grid analysis to enhance the resilience of electrical distribution systems by providing accurate and efficient risk assessments. Utilizing real-time observations from Distributed Fiber Optic Sensing (DFOS), it accurately evaluates risks associated with probable events and calculates the risk of line failures. The method develops a modified risk-aware system that incorporates minimized system loss, voltage violations, power flow violations, the number of switching operations, and radiality constraints. By integrating DFOS data with existing grid data, the method enables rapid system adaptation and localized fault detection, advancing the state of the art in grid resilience and reliability.

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: Disclosed are systems and methods that employ distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) to monitor and provide real-time estimation of ice thickness on fiber optic communications facilities, and which integrate DSA data with a hybrid processing technique that combines frequency domain decomposition (FDD) and stochastic subspace identification (SSI). Aspects of our innovative systems and methods include: i) Hybrid Signal Processing Techniques; ii) Real-time, Continuous Ice Monitoring; iii) Enhanced Noise Robustness and Non-linear Dynamics Handling; and iv) Adaptability and Scalability.

Abstract: Disclosed are systems and methods that employ distributed fiber optic sensing (DFOS)/distributed temperature sensing (DTS) to measure/monitor soil moisture and seepage at sub-meter spatial resolution over a large geographic area. In sharp contrast to the prior art which generally employed many, point soil moisture sensors, systems and methods according to aspects of the present disclosure employ DTS with an fiber optic sensor that advantageously senses soil temperature at sub-meter spatial resolution continuously. From these continuous, wide areas, high resolution DTS measurements, soil moisture and seepage determinations of soil in which the fiber optic sensor contacts are made.

Abstract: Disclosed are systems and methods employing distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) in conjunction with mathematical algorithms, and an optional camera to determine layout of subsea cables. The subsea cables are pressure wave detectors of the DAS system and employ mathematical algorithms which map detected pressure waves to sea waves to determine subsea cable layout. When a subsea cable is installed below the seabed, pressure changes due to the waves on the sea surface are still detected. When DAS data is integrated with an optional camera visual input namely: a relative subsea cable layout may be determined by assuming a uniform planar sea wave propagating through the sea; an absolute cable layout may be determined by considering actual sea wave shape detected by the camera; and wave shapes may be determined based on a known subsea cable layout.

Abstract: Disclosed is an artificial intelligence-based platform for infrastructure inspection, localization and maintenance of power grid structures that advantageously allows electrical utility service providers to evaluate the condition of distribution grid components at scale before failures. Our inventive systems, and methods of the platform employ a vehicle-mounted, camera-based inspection system that passively collects maintenance-related data during routine operation. A vehicle mounted computing platform—coupled with multiple sensors including stereo cameras, inertial measurement unit, and global positioning system—are combined with AI powered software that provides multiple monitoring applications including utility pole detection, pole GPS location, situational awareness/object recognition of any pole-mounted instruments, and anomaly detection resulting from weather-related or other events.

Abstract: A large language model (LLM) powered voice-to-data documentation system and operating method for field inspection and infrastructure assessment which converts spoken language into precise, structured digital data in real-time and overcomes limitations of manual notetaking and data transcription by providing real-time, accurate interpretation of technical terminology and context, significantly reducing human error and enhancing data integrity. Our system and method provide immediate decision-making and problem-solving, markedly improving the speed and efficiency of infrastructure maintenance and compliance processes and may be portable, thereby permitting their use in challenging field environments, enabling inspectors to focus on critical assessment tasks without the distraction of cumbersome documentation procedures.

Abstract: Disclosed are DFOS/DTS systems, methods, and structures that employ physics-informed machine learning, Finite Element Analysis (FEA) in combination with DFOS/DTS to enhance the detection, prediction, and management of thermal anomalies in submarine cables. Our integrated approach advantageously leverages FEA to simulate accurate temperature distributions within the cable, identifies potential hot spots, and validates these with real-time DTS data. By integrating advanced machine learning algorithms, our systems and methods continuously learn from both simulated and real-world data, predicting potential failure points and suggesting preemptive maintenance actions. A hybrid model, combining data-driven and physics-based approaches, incorporates uncertainty quantification methods, providing confidence intervals for predictions.

Abstract: Disclosed are systems and methods employing distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) that continuously monitor the buried/exposed status of undersea power cables for offshore wind farms. Our DAS systems and methods continuously monitor dynamic conditions characterizing undersea power cables and advantageously detect dynamic strain of an optical fiber that may be part of a composite cable with the power cable. By measuring and monitoring these dynamic conditions, a change in status from buried to exposed is determined and service personnel deployed before damage occurs to the power cable.

Abstract: Disclosed are systems and methods that employ distributed fiber optic sensing (DFOS)/distributed temperature sensing (DTS) to measure/monitor soil moisture and seepage at sub-meter spatial resolution over a large geographic area. In sharp contrast to the prior art which generally employed many, point soil moisture sensors, systems and methods according to aspects of the present disclosure employ DTS with an fiber optic sensor that advantageously senses soil temperature at sub-meter spatial resolution continuously. From these continuous, wide areas, high resolution DTS measurements, soil moisture and seepage determinations of soil in which the fiber optic sensor contacts are made.

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: Disclosed are integrated DFOS/DAS systems, methods, and structures that advantageously detecting fuse cutoff blowing events using existing telecom cables. Our systems, methods, and structures employ an exciter to broadcast acoustic signal tracks and evaluated on wooden utility poles within a real-scale testbed, simulating fuse cutoff blowing events in the power grid. A Distributed Acoustic Sensing (DAS) system connected to an optical fiber sensor cable collects a 2D waterfall matrix. A frequency learning model is subsequently used to identify these acoustic events based on the results of frequency analysis.

Abstract: Disclosed is an integrated DFOS system and method for enhanced offshore wind turbine monitoring using physics-informed machine learning algorithms that advantageously utilizes existing optical fiber communication cables, distributed fiber optic sensing (DFOS), Physics-informed machine learning algorithms, monitoring of critical underwater components, integrated data processing (DPU), and comprehensive monitoring.

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.