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Cynthia Rudin
Location:
United States
Cynthia D Rudin, 47737 E Franklin St, Chapel Hill, NC 27514
Cynthia Rudin Phones & Addresses
737 E Franklin St, Chapel Hill, NC 27514
48 Harrison St, Brookline, MA 02446
20 Waterside Plz, New York, NY 10010
133 Old Farm Cir, Buffalo, NY 14221
Williamsville, NY
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Us Patents
Machine Learning For Power Grid
US Patent:
2013023, Sep 5, 2013
Filed:
Jan 15, 2013
Appl. No.:
13/742124
Inventors:
Cynthia Rudin - New York NY, US
David Waltz - Princeton NJ, US
Maggie Chow - Hartsdale NY, US
Haimonti Dutta - Trenton NJ, US
Phil Gross - Brooklyn NY, US
Huang Bert - Silver Spring MD, US
Steve Ierome - New York NY, US
Delfina Isaac - New York NY, US
Arthur Kressner - Westfiled NJ, US
Rebecca J. Passonneau - New York NY, US
Axinia Radeva - New York NY, US
Leon L. Wu - New York NY, US
Peter Hofmann - Hasbrouck Heights NJ, US
Frank Dougherty - Yorktown Heights NY, US
David Waltz - Princeton NJ, US
Maggie Chow - Hartsdale NY, US
Haimonti Dutta - Trenton NJ, US
Phil Gross - Brooklyn NY, US
Huang Bert - Silver Spring MD, US
Steve Ierome - New York NY, US
Delfina Isaac - New York NY, US
Arthur Kressner - Westfiled NJ, US
Rebecca J. Passonneau - New York NY, US
Axinia Radeva - New York NY, US
Leon L. Wu - New York NY, US
Peter Hofmann - Hasbrouck Heights NJ, US
Frank Dougherty - Yorktown Heights NY, US
Assignee:
Consolidated Edison Company of New York - New York NY
The Trustees of Columbia University in the City of New York - New York NY
The Trustees of Columbia University in the City of New York - New York NY
International Classification:
G06N 99/00
US Classification:
706 12
Abstract:
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.
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