International Business Machines Corporation

Utopus Insights, Inc.

Tarun Kumar

Younghun Kim

Abhishek Raman

Rui Zhang

Harsh Chaudhary

Aanchal Aggarwal

Mark Alan Lavin

Kevin Wilson Warren

Wander S Wadman

Aanchal Goyal

Varun Badrinath Krishna

Yakup Koç

Arun Hampapur

Ildar Khabibrakhmanov

Ronald Ambrosio

Guruprasad Srinivasan

Gyana Ranjan Parija

Aleksandr Y Aravkin

Haifeng Xi

Giuliano Andrea Pagani

Fook-Luen Heng

Shilpa N Mahatma

Heng Cao

Chen Jiang

Michael Adendorff

David L Jensen

Mehmet F Candas

Wei Zhang

Chandramouli Visweswariah

Bo Zhang

Maxim I Sviridenko

Kevin McKenzie

Tracy Jay Kimbrel

Srivats Shukla

Minwei Feng

Richard D Prewitt, Jr

Debra Tomkowid

Jefferson Huang

A method for creating a spatio-temporal schedule includes receiving a set of available work crews and a set of workorders to be scheduled, sequencing the set of workorders, wherein each workorder of the set of workorders is associated with a geographic location, a start time and an end time, and iterating through the set of available work crews, assigning a plurality of workorders of the set of workorders fully allocating a current crew before selecting a next crew, until all work crews are fully allocated and/or all workorders are assigned, wherein the assignment of workorders for a given work crew generates a route comprising the plurality of workorders.

Spatio-temporal approach to scheduling field operations

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

An example method comprises receiving first historical data of a first time period and failure data, identifying at least some sensor data that was or potentially was generated during a first failure, removing the at least some sensor data to create filtered historical data, training a classification model using the filtered historical data, the classification model indicating at least one first classified state at a second period of time prior to the first failure indicated by the failure data, applying the classification model to second sensor data to identify a first potential failure state based on the at least one first classified state, the second sensor data being from a subsequent time period, generating an alert if the first potential failure state is identified based on at least a first subset of sensor signals generated during the subsequent time period, and providing the alert.

System and method for predicting failure of components using temporal scoping of sensor data

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

An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.

System and method for fault detection of components using information fusion technique

A system for forecasting leaks in a fluid-delivery pipeline network. The system identifies a subsystem in the pipeline network that comprises a plurality of topologically connected stations. The system accesses historical temporal sensor measurements of a plurality of variables of the stations that are directly connected and generates a temporal causal dependency model for a first control variable at the first station in the subsystem, based on the plurality of time series of sensor measurements of a second variable of the first station, and temporal delay characteristics of the plurality of time series of sensor measurements of the second variable at the stations directly connected to the first station. The system automatically calculates a normal operating value of the first control variable at the first station and the deviations between actual measured values and the normal operating value and determines a threshold deviation that indicates a leak event.

System and method for forecasting leaks in a fluid-delivery pipeline network

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 locating an anomaly in a fluid transmission pipeline system is provided. The method may include receiving data for one or more physical conditions measured at an input and output of a pipeline portion, performing multiple simulations on a model of the pipeline portion to determine sets of simulated conditions that respectively correspond to simulated leak locations, determining a probability for a leak at one or more of the simulated leak locations by comparing one or more sets of simulated conditions to the received output data, and determining a highest probability location for the leak based on the probability for the leak at the one or more of the simulated leak locations. At least one simulation of the multiple simulations may be performed as a stochastic process and may be based on the received input data.

Anomaly localization in a pipeline

Embodiments are directed to a computer-implemented method, computer system, and computer program product for forecasting power generation. The method includes receiving a first power generation forecast. A second power generation forecast is received, along with actual observed power generation. A model is created for the observed power generation based on the second power generation forecast. A third power generation forecast is created using the model for the observed power generation, the second power generation forecast, and the first power generation forecast.

Forecasting solar power generation using real-time power data

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Mohegan Lake, NY, United States of America

Tarun Kumar