公开(公告)号：US20170300605A1

公开(公告)日：2017-10-19

申请号：US15132884

申请日：2016-04-19

Applicant: General Electric Company

Inventor： Paul Alex ARDIS ,  Subhankar GHOSH ,  Alexander Turner GRAF

IPC: G06F17/50 ,  G06N99/00 ,  G06F17/11

CPC classification number: G06N99/005 ,  G05B23/024 ,  G06K9/6259

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, create 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.

公开(公告)号：US20170286854A1

公开(公告)日：2017-10-05

申请号：US15085491

申请日：2016-03-30

Applicant: General Electric Company

Inventor： Paul Alex ARDIS ,  Yunwen XU

IPC: G06N7/00 ,  G06N5/02

CPC classification number: G06N5/045 ,  G06F11/008 ,  G06F11/3447 ,  G06F17/50 ,  G06N3/126

Abstract: A method for automatic revision of a predictive damage model that assess a physical system and provides maintenance recommendations to a user platform includes evaluating the predictive model at periodic intervals, identifying alternate parameters for the model that satisfy real-world physical constraints, determining an impact of the alternate parameters on an accuracy of the predictive model, forecasting performance of a predictive model modified with the alternate parameters, optimizing data driven terms of the predictive model by deploying the alternate parameters, and providing updated maintenance recommendations to a user platform display based on the modified predictive model. The method can also include comparing the one or more maintenance recommendations to actual maintenance experiences on the physical system, and also performing a heuristic parameter search for the data-driven terms. A non-transitory computer readable medium containing executable instructions and a system for implementing the method are also disclosed.