SYSTEM AND METHOD FOR DAMAGE PROPAGATION ESTIMATION

• US 20080140360A1
• Filed: 12/07/2006
• Published: 06/12/2008
• Est. Priority Date: 12/07/2006
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Yan, Weizhong, Goebel, Kai Frank, Eklund, Neil Holger White, Qiu, Hai
• Granted Patent: US 7,933,754 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06
• CPC Class Codes: G06F 2111/10 Numerical modelling
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30

Abstract

A method to estimate damage propagation is disclosed. The method includes making available a set of input parameters to a computational model, executing the computational model with defined changes within a range of an input parameter of the set of input parameters to define a range of at least one modeled output, receiving at least one signal responsive to and representative of a respective one of an actual sensor output, and estimating damage propagation based upon a correlation of the received signal to the modeled output.

Claims

1. A method to estimate damage propagation comprising:

making available a set of input parameters to a computational model;

executing the computational model with defined changes within a range of an input parameter of the set of input parameters to define a range of at least one modeled output;

receiving at least one signal responsive to and representative of a respective one of an actual sensor output;

estimating damage propagation based upon a correlation of the received signal to the modeled output; and

generating a signal corresponding to the damage propagation.

2. The method of claim 1, wherein the estimating comprises:

assessing a plurality of operational data prior to an end of equipment useful life.

3. The method of claim 1, wherein:

the making available comprises supplying a first range of a first input parameter and a second range of a second input parameter; and

the executing comprises defining a response map of the modeled output in response to defined changes within the first range of the first input parameter and defined changes within the second range of the second input parameter.

4. The method of claim 3, wherein:

the making available comprises supplying the first range of an efficiency parameter of a turbine engine and the second range of a flow parameter of the turbine engine; and

the executing comprises defining the response map in response to defined changes within the first range of the efficiency parameter and defined changes within the second range of the flow parameter.

5. The method of claim 3, wherein the receiving comprises receiving more than one signal responsive to and representative of more than one actual sensor output and the executing comprises defining more than one response map, each of the more than one response maps corresponding to a respective one of the more than one received signals, the method further comprising:

normalizing each of the more than one received signals and corresponding more than one response maps with respect to a reference value;

superimposing the more than one normalized received signals and corresponding more than one normalized response maps; and

determining a collective best match of the more than one superimposed normalized received signals with the corresponding more than one superimposed normalized response maps.

6. The method of claim 5, wherein the determining comprises:

estimating a health related pairing.

7. The method of claim 5, wherein the determining comprises applying a function:

min(Σ

w_i(dist_i)²), i ε

{characteristic}

wherein;

w_i represents a weighting function

dist_i represents a distance from the more than one superimposed normalized received signals to the corresponding more than one superimposed normalized response maps; and

characteristic represents an operating characteristic corresponding to each of the more than one received signals.

8. The method of claim 5, further comprising:

defining a change of the best match in response to a change in the more than one received signal; and

measuring a trajectory and a rate of the change of the best match.

9. The method of claim 8, further comprising:

determining a direction of the change of the best match; and

interpreting the direction to define a diagnostic finding.

10. The method of claim 8, further comprising:

defining a health index based upon a most limiting operational margin of a set of operational margins; and

projecting the trajectory of the change of the best match into a one-dimensional margin domain comprising the health index to define a minimal margin trajectory over time, thereby providing a health assessment curve.

11. The method of claim 10, further comprising:

extrapolating the health assessment curve to predict a remaining useful equipment life.

12. The method of claim 11, wherein:

the extrapolating comprises extrapolating the health assessment curve to a value of the health index equal to zero.

13. The method of claim 11, wherein:

the extrapolating comprises fitting the health assessment curve to an exponentially decaying curve function.

14. The method of claim 11, further comprising:

discretizing a pairing of the first input parameter and the second input parameter relating to the extrapolated health assessment curve;

executing the computational model with the discretized pairing to define the modeled output; and

comparing the received signal with a corresponding modeled output.

15. The method of claim 14, further comprising:

establishing a confidence level based upon the comparing the received signal with the corresponding modeled output defined in response to the discretized pairing.

16. A program storage device readable by a computer, the device embodying a program or instructions executable by the computer to perform the method of claim 1.

17. A system for estimating damage propagation, the system comprising:

a database comprising a set of input parameters;

a processor in signal communication with the database; and

a computational model application for executing on the processor, the computational model performing a method, the method comprising;

defining a range of at least one modeled output based upon a range of an input parameter of the set of input parameters;

receiving at least one signal responsive to and representative of a respective one of an actual sensor output;

estimating damage propagation based upon a correlation of the received signal to the modeled output; and

generating a signal corresponding to the damage propagation.

18. The system of claim 17, wherein:

the estimating comprises assessing a plurality of operational data prior to an end of equipment useful life.

19. The system of claim 17, wherein:

the set of input parameters comprises a first range of a first input parameter and a second range of a second input parameter; and

the defining the modeled sensor output comprises defining a response map of the modeled output in response to defined changes within the first range of the first input parameter and defined changes within the second range of the second input parameter.

20. The system of claim 19, wherein:

the set of input parameters comprise the first range of an efficiency parameter of a turbine engine and the second range of a flow parameter of the turbine engine; and

the defining the response map comprises defining the response map in response to defined changes within the first range of the efficiency parameter and defined changes within the second range of the flow parameter.

21. The system of claim 19, wherein the receiving comprises receiving more than one signal responsive to and representative of more than one actual sensor output and the defining comprises more than one response map, each of the more than one response maps corresponding to the respective one of the more than one received signal, wherein the computational model application further performs:

normalizing each of the more than one received signals and corresponding more than one response maps with respect to a reference value;

superimposing the more than one normalized received signals and corresponding more than one normalized response maps;

determining a collective best match of the more than one superimposed normalized received signals with the corresponding more than one superimposed normalized response maps;

defining a change of the best match in response to a change in the more than one received signal; and

measuring a trajectory and a rate of the change of the best match.

22. The system of claim 21, wherein the computational model application further performs:

defining a health index based upon a most limiting operational margin of a set of operational margins; and

projecting the trajectory of the change of the best match into a one-dimensional margin domain comprising the health index to define a minimal margin trajectory over time, thereby providing a health assessment curve.

23. The system of claim 22, wherein the computational model application further performs:

extrapolating the health assessment curve to predict a remaining useful equipment life.

24. The system of claim 23, wherein the computational model application further performs:

discretizing a pairing of the first input parameter and the second input parameter relating to the extrapolated health assessment curve;

defining the modeled output in response to the discretized pairing; and

comparing the received signal with a corresponding modeled output defined in response to the discretized pairing.

25. The system of claim 24, wherein the computational model application further performs:

establishing a confidence level based upon the comparing the received signal with the corresponding modeled output.