Utopus Insights, Inc.

International Business Machines Corporation

Harsh Chaudhary

Younghun Kim

Tarun Kumar

Abhishek Raman

Rui Zhang

Aanchal Aggarwal

Yakup Koç

A system and method of managing a network that includes assets are described. The method includes modeling the network as a directed graph with each of the assets represented as a node and determining alternative paths to each node from each available corresponding source of the node. The method also includes computing upstream robustness of each node, computing upstream robustness of the network, and computing downstream criticality of each node. Managing the network and each asset of the network is based on the upstream robustness and the downstream criticality of each node.

Network management based on assessment of topological robustness and criticality of assets

Technical solutions are described for predicting linepack delays. An example method includes receiving temporal sensor measurements of a first fluid-delivery pipeline network and generating a causality graph of the first fluid-delivery pipeline network. The method also includes determining a topological network of the stations based on the causality graph, where the topological network identifies a temporal delay between a pair of stations. The method also includes generating a temporal delay prediction model based on the topological network and predicting the linepack delays of a second fluid-delivery pipeline network based on the temporal delay prediction model, where a compressor station of the second fluid-delivery pipeline network compresses fluid based on the predicted linepack delays to maintain a predetermined pressure.

Linepack delay measurement in fluid delivery pipeline

Technical solutions are described for determining topological connectivity between stations of a fluid-delivery pipeline network. An example method includes receiving temporal sensor measurements of the fluid-delivery pipeline network, that include a series of sensor measurements from each respective station of the fluid-delivery pipeline network. The method also includes generating a causality graph of the fluid-delivery pipeline network based on the temporal sensor measurements, where the causality graph includes a set of nodes and a set of links, where the nodes are representative of the stations, and a pair of nodes is connected by a link in response to the pair of stations being temporally dependent. The method also includes determining a topological network of the stations based on the causality graph, where the topological network identifies one or more destination stations for a supply station in the fluid-delivery pipeline network.

Topological connectivity and relative distances from temporal sensor measurements of physical delivery system

A method and system method to detect a leak within a pipeline network include measuring pressure at each of a plurality of sensors distributed along the pipeline network as a time-varying pressure signal. Tuning a model is based on gas mass conservation law in the absence of the leak, the tuning including obtaining the time-varying pressure signal from each of the plurality of sensors, and monitoring the time-varying pressure signals is done to detect the leak based on the model.

Leak identification in a mass transport network

Technical solutions are described for forecasting leaks in a pipeline network. An example method includes identifying a subsystem in the pipeline network that includes a first station. The method also includes accessing historical temporal sensor measurements of the stations. The method also includes generating a prediction model for the first station that predicts a pressure measurement at the first station based on the historical temporal sensor measurements at each station in the subsystem. The method also includes predicting a series of pressure measurements at the first station based on the historical temporal sensor measurements. The method also includes determining a series of deviations between the series of pressure measurements and historical pressure measurements of the first station and identifying a threshold value from the series of deviations, where a pressure measurement at the first station above or below the threshold value is indicative of a leak in the subsystem.

Forecasting leaks in pipeline network

A method and system to localize a leak in a pipeline network include identifying a respective measured time at which an effect of a leak exhibits in a time-varying data signal measured by each of a plurality of sensors distributed along the pipeline network, each pair of the plurality of sensors defining a respective pipe segment therebetween. Estimating a time of occurrence of the leak based on the measured time associated with two or more of the plurality of sensors; and estimating a relative distance of a leak location from one or more of the plurality of sensors. Determining the leak location is based on the relative distance.

Leak localization in pipeline network

A method and system to generate a network graph representation of a physically connected network include selecting a selected sensor among a plurality of sensors arranged along the physically connected network, searching a time-varying data signal from the selected sensor for measurement patterns; identifying candidate sensors among the plurality of sensors that are candidates for being directly connected with the selected sensor based on each of the candidate sensors outputting a respective candidate time-varying data signal with candidate patterns that match the measurement patterns of the time-varying data signal, and constructing possible graph sub-structures including the selected sensor and the candidate sensors. Determining a feasible graph sub-structure is based on the possible graph sub-structure and spatial placement of each of the selected sensor and the candidate sensors, and generating the network graph representation is by iteratively determining the feasible graph sub-structure by selecting a different sensor as the selected sensor.

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Briarcliff Manor, NY, United States of America

Harsh Chaudhary