Methods and systems for predictive modeling using a committee of models

• US 20070135938A1
• Filed: 12/08/2005
• Published: 06/14/2007
• Est. Priority Date: 12/08/2005
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Xue, Feng, Bonissone, Piero, Subbu, Rajesh
• Application Number:
  US11/297,034
• Publication Number:
  US 20070135938A1
• Time in Patent Office: Days
• US Class Current: 700/30
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G05B 17/02 electric

Abstract

Methods and systems for predictive modeling are described. In one embodiment, the method is a method for controlling a process using a committee of predictive models. The process has a plurality of control settings and at least one probe data representative of state of the process. The method includes the steps of providing probe data to each model in the model committee so that each model generates a respective output, aggregating the model outputs, and generating a predictive output based on the aggregating.

Claims

1. A method for controlling a process using a committee of predictive models, the process having a plurality of control settings and at least one probe data representative of state of the process, said method comprising the steps of:

providing probe data of the at least one probe within a prediction inputs space to each model in the model committee so that each model generates a respective output;

retrieving peers of the at least one probe, wherein the peers are within the prediction inputs space;

determining a local performance of each model by calculating outputs from each model for each peer;

aggregating the model outputs;

generating a predictive output based on said aggregating; and

transmitting the predictive output for viewing by an operator.

2. A method in accordance with claim 1 wherein each model is a neural network based data-driven model.

3. A method in accordance with claim 2 wherein each model is trained and validated using historical operational data.

4. A method in accordance with claim 1 wherein each model represents an input-output relationship.

5. A method in accordance with claim 1 wherein aggregating the model outputs comprising compensating each model output based on model performance.

6. A method in accordance with claim 5 wherein compensating is performed using at least one of:

a local weight determined for each model; and

a local weight and bias determined for each model.

7. A method in accordance with claim 6 wherein the local weight for each model is based on a mean absolute error determined using peers for each model.

8. A system for generating a predictive output related to a process, the process having a plurality of control settings and at least one probe data representative of state of the process, said system comprising:

a committee of models comprising a plurality of predictive models, each said model configured to generate a respective output based on data from a probe within a prediction inputs space; and

a computer programmed to;

retrieve peers of the probe wherein the peers are within the prediction inputs space;

determine a local performance of each model by calculating outputs from each model for each peer;

fuse the outputs from said models to generate at least one predictive output based on said model outputs; and

transmit the at least one predictive output for viewing by an operator.

9. A system in accordance with claim 8 wherein each said model is a neural network based data-driven model.

10. A system in accordance with claim 9 wherein each model is trained and validated using historical operational data.

11. A system in accordance with claim 8 wherein each model represents an input-output relationship.

12. A system in accordance with claim 8 wherein to fuse the outputs from said models, some computer is programmed to aggregate the model outputs, and generate a predictive output based on said aggregating.

13. A system in accordance with claim 12 wherein said aggregating the model outputs comprises compensating each model output based on model performance.

14. A system in accordance with claim 13 wherein said compensating is performed using at least one of:

a local weight determined for each model; and

a local weight and bias determined for each model.

15. A system in accordance with claim 14 wherein the local weight for each said model is based on a mean absolute error determined using peers for each model.

16. A computer implemented method for generating a predictive output related to a process, the process having a plurality of control settings and at least one probe data representative of state of the process, said method comprising:

supplying inputs to a committee of models comprising a plurality of predictive models;

executing each said model to generate a respective output based on data from the probe within a prediction inputs space;

retrieving peers of the probe, wherein the peers are within the prediction inputs space;

determining a local performance of each model by calculating outputs from each model for each peer;

fusing the outputs from said models to generate at least one predictive output based on said model outputs; and

transmitting the at least one predictive output for viewing by an operator.

17. A computer implemented method in accordance with claim 16 wherein each said model is a neural network based data-driven model, each said model representing an input-output relationship.

18. A computer implemented method in accordance with claim 16 wherein to fuse the outputs from said models, said method comprises aggregating the model outputs and generating a predictive output based on said aggregating.

19. A computer implemented method in accordance with claim 18 wherein said aggregating the model outputs comprises compensating each model output based on model performance.

20. A computer implemented method in accordance with claim 19 wherein said compensating is performed using at least one of:

a local weight determined for each model; and

a local weight and bias determined for each model.