Utopus Insights, Inc.

International Business Machines Corporation

Abhishek Raman

Younghun Kim

Tarun Kumar

Harsh Chaudhary

Rui Zhang

Aanchal Aggarwal

Yakup Koç

Mark Alan Lavin

Giuliano Andrea Pagani

Chandramouli Visweswariah

A system and method of managing a network with assets are described. The method includes generating a directed graph with each of the assets represented as a node, determining individual failure probability of each node, computing downstream failure probability of each node according to an arrangement of the nodes in the directed graph, computing upstream failure probability of each node according to the arrangement of the nodes in directed graph, and computing network failure probability for each node based on the corresponding individual failure probability, the downstream failure probability, and the upstream failure probability. Managing the network is based on the network failure probability of the assets.

Network management based on modeling of cascading effect of failure

A system and method to schedule inspections for a plurality of assets at a corresponding plurality of outdoor locations are described. The method includes retrieving location attributes of each of the plurality of assets, retrieving terrestrial geotagged images that include one or more of the plurality of assets, obtaining parameters associated with each of the plurality of assets based on information from the images, and determining risk factors associated with each of the plurality of assets based on the associated parameters and a failure mapping. The method also includes determining a risk score for each of the plurality of assets based on the corresponding risk factors, and scheduling inspections of the plurality of assets based on the risk score associated with each of the plurality of assets.

Failure modeling by incorporation of terrestrial conditions

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

A method for assessing an asset status is provided. The method may include identifying, by a processor, an asset within a plurality of tangible, deployed assets. The method may also include retrieving a plurality of images from at least one data repository, whereby the plurality of images are captured within a preconfigured distance of the identified asset. The method may further include determining a portion of the retrieved plurality of images depict the identified asset. The method may also include performing a plurality of image processing techniques on the determined portion. The method may further include creating an assessment of the asset status of the identified asset based on the performed plurality of image processing techniques, whereby the created assessment details whether the identified asset needs a repair or a replacement.

Identifying changes in health and status of assets from continuous image feeds in near real time

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.

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Santa Clara, CA, United States of America

Abhishek Raman