METHOD AND SYSTEM FOR FAULT ACCOMMODATION OF MACHINES

• US 20080229754A1
• Filed: 03/22/2007
• Published: 09/25/2008
• Est. Priority Date: 03/22/2007
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Rausch, Randal Thomas, Subbu, Rajesh Venkat, Frederick, Dean Kimball, Goebel, Kai Frank
• Granted Patent: US 7,904,282 B2
• Time in Patent Office: Days
• US Class Current: 60/772
• CPC Class Codes: F02C 9/00 Controlling gas-turbine plants; Controlling fuel supply in air- breathing jet-propulsion plants controlling air intakes F02C7/057; controlling turbines F01D; controlling compressors F04D27/00; controlling in general G05
• CPC Class Codes: F05D 2270/44 active, predictive, or anticipative
• CPC Class Codes: F05D 2270/71 synthesized, i.e. parameter computed by a mathematical model
• CPC Class Codes: Y02T 50/60 Efficient propulsion technologies, e.g. for aircraft

Abstract

A method for multi-objective fault accommodation using predictive modeling is disclosed. The method includes using a simulated machine that simulates a faulted actual machine, and using a simulated controller that simulates an actual controller. A multi-objective optimization process is performed, based on specified control settings for the simulated controller and specified operational scenarios for the simulated machine controlled by the simulated controller, to generate a Pareto frontier-based solution space relating performance of the simulated machine to settings of the simulated controller, including adjustment to the operational scenarios to represent a fault condition of the simulated machine. Control settings of the actual controller are adjusted, represented by the simulated controller, for controlling the actual machine, represented by the simulated machine, in response to a fault condition of the actual machine, based on the Pareto frontier-based solution space, to maximize desirable operational conditions and minimize undesirable operational conditions while operating the actual machine in a region of the solution space defined by the Pareto frontier.

Claims

1. A method for multi-objective fault accommodation using predictive:

modeling, the method comprising;

using a simulated machine that simulates a faulted actual machine;

using a simulated controller that simulates an actual controller, the simulated machine being controlled by the simulated controller, and the actual machine being controlled by the actual controller;

performing a multi-objective optimization process, based on specified control settings for the simulated controller and specified operational scenarios for the simulated machine controlled by the simulated controller, to generate a Pareto frontier-based solution space relating performance of the simulated machine to settings of the simulated controller, including adjustment to the operational scenarios to represent a fault condition of the simulated machine; and

adjusting control settings of the actual controller, represented by the simulated controller, for controlling the actual machine, represented by the simulated machine, in response to a fault condition of the actual machine, based on the Pareto frontier-based solution space, to maximize desirable operational conditions and minimize undesirable operational conditions while operating the actual machine in a region of the solution space defined by the Pareto frontier.

2. The method of claim 1, wherein:

the actual machine is an aircraft engine.

3. The method of claim 2, wherein:

the fault condition is a non-catastrophic HPC or HPT fault condition.

4. The method of claim 1, wherein:

the faulted condition of the simulated machine is representative of the non-catastrophic faulted condition of the actual machine.

5. The method of claim 1, wherein:

the maximizing and the minimizing are performed off-line and solutions are precompiled into lookup tables that are stored onboard such that a solution is accessible within a subsecond time frame sufficient to establish safety-restoring operating points for the actual machine, thereby accommodating the fault condition of the actual machine.

6. The method of claim 5, wherein:

the maximizing and the minimizing are further performed in a time frame greater than a subsecond subsequent to establishing the safety-restoring operating points to enable calculation of trade-offs on the Pareto frontier to incorporate contemporaneous demands on the actual machine and external demands.

7. The method of claim 6, wherein:

the contemporaneous demands on the actual machine includes one of operational safety thresholds of the actual machine, duration of operation of the actual machine, deterioration of the actual machine, risk of secondary damage, logistic considerations for the actual machine, planning considerations for the actual machine, or combinations thereof.

8. The method of claim 1, wherein:

the using and the adjusting are performed on-board the actual machine.

9. The method of claim 8, wherein:

the using and the adjusting are performed at any time between consecutive operations of the actual machine.

10. The method of claim 1, wherein:

the adjusting control settings comprises applying a bi-directional filtering algorithm to facilitate smoothness in derived schedule surfaces.

11. The method of claim 2, wherein:

the adjusting control settings comprises optimizing actuator gains, controls modifiers, and schedules of a component level model of the predictive model.

12. The method of claim 2, wherein:

the adjusting control settings serves to reduce module temperature peaks, and to increase stall margins of the aircraft engine.

13. The method of claim 1 wherein:

the performing a multi-objective optimization process is carried out in a federated manner such that subsystems find their own solutions, and subsystem intercorrelations are controlled at a system level.

17. A computer readable medium for multi-objective fault accommodation using predictive modeling, the computer readable medium comprising computer executable instructions for facilitating the method of claim 1.

14. A system for multi-objective fault accommodation using predictive modeling, the system comprising:

a simulated machine that simulates a faulted actual machine;

a simulated controller that simulates an actual controller, the simulated machine being controlled by the simulated controller, and the actual machine being controlled by the actual controller;

a processor that performs a multi-objective optimization process, based on specified control settings for the simulated controller and specified operational scenarios for the simulated machine controlled by the simulated controller, to generate a Pareto frontier-based solution space relating performance of the simulated machine to settings of the simulated controller, including adjustment to the operational scenarios to represent a fault condition of the simulated machine; and

an adjuster portion that adjusts control settings of the actual controller, represented by the simulated controller, for controlling the actual machine, represented by the simulated machine, in response to a fault condition of the actual machine, based on the Pareto frontier-based solution space, to maximize desirable operational conditions and minimize undesirable operational conditions while operating the actual machine in a region of the solution space defined by the Pareto frontier.

15. The system of claim 14, wherein:

the actual machine is an aircraft engine.

16. The system of claim 15, wherein:

the fault condition is a non-catastrophic HPC or HPT fault condition.