Utopus Insights, Inc.

International Business Machines Corporation

Yajuan Wang

Younghun Kim

Gabor Solymosi

Shilpa N Mahatma

Gigi Y C Yuen-Reed

Francisco Phelan Curbera

Ede Szarka

Fei Wang

Chandramouli Visweswariah

Jianying Hu

Rose Marie Williams

Lars Toghill

Joel Igba

Neil Desai

Ning Li

Fang Lu

Fei Liu

Qiqing Christine Ouyang

Seshadri Subbanna

Steven I Ross

Kenney Ng

Hung-Yang Chang

Adam Perer

Brian Arffa

Hyuna Yang

Rose M Williams

Chuanren Liu

Solymosi Gabor

Ede Szyrka

An example method utilizing different pipelines of a prediction system, comprises receiving event and alarm data from event logs, failure data, and asset data from Supervisory Control and Data Acquisition (SCADA) system(s), retrieving patterns of events, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receiving current sensor data from the sensors of the components, applying the selected model(s) to the current sensor data to generate a component failure prediction, comparing the component failure prediction to a threshold, and generating an alert and report based on the comparison to the threshold.

Scalable system and engine for forecasting wind turbine failure

An example method comprises receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

System and method for evaluating models for predictive failure of renewable energy assets

An example method comprises receiving historical wind turbine failure data and asset data from SCADA systems, receiving first historical sensor data, determining healthy assets of the renewable energy assets by comparing signals to known healthy operating signals, training at least one machine learning model to indicate assets that may potentially fail and to a second set of assets that are operating within a healthy threshold, receiving first current sensor data of a second time period, applying a machine learning model to the current sensor data to generate a first failure prediction a failure and generate a list of assets that are operating within a healthy threshold, comparing the first failure prediction to a trigger criteria, generating and transmitting a first alert if comparing the first failure prediction to the trigger criteria indicates a failure prediction, and updating a list of assets to perform surveillance if within a healthy threshold.

Scalable systems and methods for assessing healthy condition scores in renewable asset management

A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a healthcare analytics management system. A healthcare analytics development sub-system of the healthcare analytics management system develops an analytics pipeline of a set of analytics assets for a selected healthcare based on a set of business needs for a healthcare analytics client and a healthcare analytics model based on the set of analytics assets and the set of business needs. The healthcare analytics model links to the analytics pipeline. A model deployment module of a healthcare analytics operation sub-system of the healthcare analytics management system deploys the healthcare analytics model on a set of computing devices of the selected healthcare consumer. Responsive to a model monitoring module of the healthcare analytics operation sub-system detecting a performance deviation of the deployed healthcare analytics model for performance deviation from the set of business needs for the healthcare analytics client, a model feedback module of the healthcare analytics operation sub-system determines improvement needs for the healthcare analytics model. The model feedback module feeds the improvement needs back to the healthcare analytics development sub-system. The healthcare analytics development sub-system customizes the healthcare analytics model based on the improvement needs.

Scalable and traceable healthcare analytics management

An example method utilizing different pipelines of a prediction system, comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s), retrieve patterns of events, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

A mechanism is provided to implement a healthcare analytics management system. A healthcare analytics development sub-system develops an analytics pipeline of a set of analytics assets for a selected healthcare based on a set of business needs for a healthcare analytics client and a healthcare analytics model based on the set of analytics assets and the set of business needs. A model deployment module of a healthcare analytics operation sub-system deploys the healthcare analytics model on a set of computing devices of the selected healthcare consumer. Responsive to detecting a performance deviation of the deployed healthcare analytics model from the set of business needs, a model feedback module determines improvement needs for the healthcare analytics model. The model feedback module feeds the improvement needs back to the healthcare analytics development sub-system. The healthcare analytics development sub-system customizes the healthcare analytics model based on the improvement needs.

An example method comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s), retrieve patterns of events from the SCADA data, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Scalable system and method for forecasting wind turbine failure using SCADA alarm and event logs

Aspects of the present invention include a method, system and computer program product. The method includes identifying, by a processor, a set of global risk factors for a target event using training patients, and providing, by the processor, a disease progression timeline with defined time stamps by aligning longitudinal data of the training patients based on the defined time stamp of risk targets. The method also includes positioning, by the processor, a target patient at one of the defined time stamps on the disease progression timeline, and identifying, by the processor, at least one of the training patients similar to the target patient with the same one of the defined time stamps on the disease progression timeline. The method further includes calculating, by the processor, a time-varying predictive pattern of at least a portion of the global set of risk factors for the target patient along the disease progression timeline.

Identifying personalized time-varying predictive patterns of risk factors

Embodiments of the present invention disclose a method, a computer program product, and a computer system for enriching data. A computer receives a limited dataset and a target insight. In addition, the computer identifies an applicable richer dataset and determines a population relatedness between the limited dataset and the applicable richer dataset. Moreover, the computer calculates estimators of features using the richer dataset as well as calculates estimators for features using the limited dataset. The computer then updates the estimators of the limited dataset using estimators of the richer dataset and evaluates the updated estimators. Lastly, the computer provides a data collection recommendation as it relates to the limited dataset based on the evaluation.

Limited data enricher

In one embodiment, a computer-implemented method includes transforming a plurality of electronic health records into a plurality of temporal graphs indicating an order in which events observed in the plurality of electronic health records occur and learning a temporal pattern from the plurality of temporal graphs, wherein the temporal pattern indicates an order of events that is observed to occur repeatedly across the plurality of temporal graphs.

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White Plains, NY, United States of America

Yajuan Wang