05-09-2013 дата публикации
Номер: US20130232094A1
Автор:
Roger N. Anderson,
Albert Boulanger,
Cynthia Rudin,
David Waltz,
Ansaf Salleb-Aouissi,
Maggie Chow,
Haimonti Dutta,
Phil Gross,
Huang Bert,
Steve Ierome,
Delfina Isaac,
Arthur Kressner,
Rebecca J. Passonneau,
Axinia Radeva,
Leon L. Wu,
Peter Hofmann,
Frank Dougherty
A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid. 1. A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid comprising:(a) a raw data assembly to provide raw data representative of the like components within the electrical grid;(b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques;(c) a database, operatively coupled to the data processor, to store the more uniform data;(d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components;(e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered ...
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