Method and system for performing model-based multi-objective asset optimization and decision-making

• US 20060271210A1
• Filed: 04/28/2005
• Published: 11/30/2006
• Est. Priority Date: 04/28/2005
• Status: Active Grant

• Current Assignee: Baker Hughes, a GE Company, LLC (Baker Hughes Company)
• Original Assignee: General Electric Company
• Inventors: Yan, Weizhong, Schmid, James J., Bonissone, Piero P., Iyer, Naresh S., Eklund, Neil H., Knodle, Chad E., Subbu, Rajesh V., Shah, Rasiklal P.
• Granted Patent: US 7,536,364 B2
• Time in Patent Office: Days
• US Class Current: 700/44
• CPC Class Codes: G06F 18/2113 by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

A method and system for performing model-based multi-objective asset optimization and decision-making is provided. The method includes building at least two predictive models for an asset. The building includes categorizing operational historical data via at least one of: controllable variables, uncontrollable variables, output objectives, and constraints. The building also includes selecting at least two output objectives or constraints, and identifying at least one controllable or uncontrollable variable suitable for achieving the at least two output objectives or constraints. The method also includes validating each predictive model and performing multi-objective optimization using the predictive models. The multi-objective optimization includes specifying search constraints and applying a multi-objective optimization algorithm. The method further includes generating a Pareto Frontier, and selecting a Pareto optimal input-output vector.

Claims

1. A method for performing multi-objective asset optimization and decision-making using predictive modeling, comprising:

building at least two predictive models for an asset, comprising;

categorizing operational historical data by at least one of;

controllable variables;

uncontrollable variables;

output objectives; and

constraints;

selecting at least two output objectives or constraints; and

identifying at least one controllable or uncontrollable variable suitable for achieving the at least two output objectives or constraints;

inputting the at least one controllable or uncontrollable variable to each of the at least two predictive models;

validating each predictive model;

if results of the validating indicate a confidence level above a specified threshold, applying a live data stream of inputs to the predictive models;

if results of the validating indicate a confidence level at or below a specified threshold, selecting at least one alternative controllable or uncontrollable variable for input to the predictive models;

performing multi-objective optimization using the predictive models, comprising;

specifying search constraints, comprising;

upper and lower bounds for each input variable;

tolerance levels representing a range of values for achieving optimal output objectives, and constraints;

applying a multi-objective optimization algorithm;

generating a Pareto Frontier, the Pareto Frontier including optimal input-output vectors; and

selecting a Pareto optimal input-output vector.

2. The method of claim 1, wherein the controllable variable includes input parameters that are changeable.

3. The method of claim 1, wherein the uncontrollable variable includes input parameters that are static.

4. The method of claim 1, wherein the output objective includes a target goal for a process.

5. The method of claim 1, wherein the constraint includes a restriction placed on an output.

6. The method of claim 1, wherein the validating is accomplished by comparing predicted results of the inputting to actual results of inputting.

7. The method of claim 1, wherein the multi-objective optimization algorithm is at least one of:

an evolutionary algorithm;

a gradient-based algorithm; and

a hybrid algorithm;

Pareto Frontier generator.

8. The method of claim 1, further comprising selecting one of the Pareto optimal input-output vectors for deployment.

9. The method of claim 1, further comprising applying a decision function to results of the generating a Pareto Frontier, wherein selecting an input-output vector for deployment is based upon results of the applying a decision function capable of reducing the number of input-output vectors.

10. The method of claim 9, further comprising selecting one of the Pareto optimal input-output vectors for deployment that minimally perturbs the asset from its existing controllable variables configuration.

11. A system for performing multi-objective asset optimization and decision-making using predictive modeling, comprising:

a processor;

a network in communication with the processor; and

a process manager implemented by the processor, the process manager performing;

building at least two predictive models for an asset, comprising;

categorizing operational historical data by at least one of;

controllable variables;

uncontrollable variables;

output objectives; and

constraints;

selecting at least two output objectives or constraints; and

identifying at least one controllable or uncontrollable variable suitable for achieving the at least two output objectives or constraints;

inputting the at least one controllable or uncontrollable variable to each of the at least two predictive models;

validating each predictive model;

if results of the validating indicate a confidence level above a specified threshold, applying a live data stream of inputs to the predictive models;

if results of the validating indicate a confidence level at or below a specified threshold, selecting at least one alternative controllable or uncontrollable variable for input to the predictive models;

multi-objective optimization using the predictive models, comprising;

specifying search constraints, comprising;

upper and lower bounds for each input variable;

tolerance levels representing a range of values for achieving optimal output objectives, and constraints;

applying a multi-objective optimization algorithm;

generating a Pareto Frontier, the Pareto Frontier including optimal input-output vectors; and

selecting a Pareto optimal input-output vector.

12. The system of claim 11, wherein the controllable variable includes input parameters that are changeable.

13. The system of claim 11, wherein the uncontrollable variable includes input parameters that are static.

14. The system of claim 11, wherein the output objective includes a target goal for a process.

15. The system of claim 11, wherein the constraint includes a restriction placed on an output.

16. The system of claim 11, wherein the validating is accomplished by comparing predicted results of the inputting to actual results of inputting.

17. The system of claim 11, wherein the multi-objective optimization algorithm is at least one of:

an evolutionary algorithm;

a gradient-based algorithm; and

a hybrid algorithm;

Pareto Frontier generator.

18. The system of claim 11, wherein the process manager further performs selecting one of the Pareto optimal input-output vectors for deployment.

19. The system of claim 11, wherein the process manager further performs applying a decision function to results of the generating a Pareto Frontier, wherein selecting an input-output vector for deployment is based upon results of the applying a decision function capable of reducing the number of input-output vectors.

20. The system of claim 19, wherein the process manager further performs selecting one of the Pareto optimal input-output vectors for deployment that minimally perturbs the asset from its existing controllable variables configuration.