Publication number: 20130232094
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
Type:
Application
Filed:
January 15, 2013
Publication date:
September 5, 2013
Applicants:
Consolidated Edison Company of New York, The Trustees of Columbia University in the City of New York
Inventors:
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
