- Patent Title: Creating predictive damage models by transductive transfer learning
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Application No.: US15132884Application Date: 2016-04-19
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Publication No.: US10229369B2Publication Date: 2019-03-12
- Inventor: Paul Alex Ardis , Subhankar Ghosh , Alexander Turner Graf
- Applicant: General Electric Company
- Applicant Address: US NY Schenectady
- Assignee: GENERAL ELECTRIC COMPANY
- Current Assignee: GENERAL ELECTRIC COMPANY
- Current Assignee Address: US NY Schenectady
- Agency: Buckley, Maschoff & Talwalkar LLC
- Main IPC: G06N99/00
- IPC: G06N99/00 ; G05B23/02 ; G06K9/62

Abstract:
A method for creating predictive damage models includes receiving a first predictive damage model, identifying latent space between a first and a second domain asset, building a regression model from first domain asset projected source data, creating target dependent variables of a second model, applying classification or regression techniques to determine a function expressing the dependent variables, determining data points from the function to develop a second regression model, applying the second regression model to data points to predict target dependent variables, evaluating the second predictive damage model using the predicted target dependent variables, performing a sensitivity study to determine a directionality parameter of the second predictive damage model, and if the results are within an acceptable predetermined range, providing maintenance or servicing recommendations generated by the second predictive model to a user platform display, else repeating the process by rebuilding the regression model to further refine the regression model.
Public/Granted literature
- US20170300605A1 CREATING PREDICTIVE DAMAGE MODELS BY TRANSDUCTIVE TRANSFER LEARNING Public/Granted day:2017-10-19
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