Component adaptive life management

• US 8,494,810 B2
• Filed: 06/07/2010
• Issued: 07/23/2013
• Est. Priority Date: 06/05/2009
• Status: Active Grant

• Current Assignee: Jentek Sensors, Inc.
• Original Assignee: Jentek Sensors, Inc.
• Inventors: Goldfine, Neil J., Sheiretov, Yanko K., Washabaugh, Andrew P., Zilberstein, Vladimir A., Grundy, David C., Jablonski, David A., Spencer, Floyd W., Lyons, Robert J.
• Primary Examiner(s): Bui, Bryan
• Application Number:
  US12/795,538
• Publication Number:
  US 20110060568A1
• Time in Patent Office: 1,142 Days
• Field of Search: 702/183, 702/34, 703/2, 703/6, 736/02, 737/99, 324/225, 324/228
• US Class Current: 702/183
• CPC Class Codes: G01N 2203/0212 Theories, calculations
• CPC Class Codes: G07C 3/00 Registering or indicating the condition or the working of machines or other apparatus, other than vehicles

Abstract

A framework for adaptively managing the life of components. A sensor provides non-destructive test data obtained from inspecting a component. The inspection data may be filtered using reference signatures and by subtracting a baseline. The filtered inspection data and other inspection data for the component is analyzed to locate flaws and estimate the current condition of the component. The current condition may then be used to predict the component'"'"'s condition at a future time or to predict a future time at which the component'"'"'s condition will have deteriorated to a certain level. A current condition may be input to a precomputed database to look up the future condition or time. The future condition or time is described by a probability distribution which may be used to assess the risk of component failure. The assessed risk may be used to determine whether the part should continue in service, be replaced or repaired. A hyperlattice database is used with a rapid searching method to estimate at least one material condition and one usage parameter, such as stress level for the component. The hyperlattice is also used to rapidly predict future condition, associated uncertainty and risk of failure.

Claims

1. An inspection system comprising:

a sensor for collecting first spatial data from a component, the first spatial data comprising a first sensor response for at least one location on the component; and

a computing system comprising;

an inspection archive comprising second spatial data having a second sensor response for the component and third spatial data having a third sensor response for the component, wherein the first, second and third spatial data are a set of spatial data;

a database comprising a plurality of data points, each data point storing a material condition of the component and a time;

a filtering module to spatially register the set of spatial data;

an estimation module to compute a first change in the material condition from the spatially registered set of spatial data, compute a second change in the material condition from the spatially registered set of spatial data, and estimate a current condition of the component based at least in part on the first change; and

a prediction module to predict a future condition of the component, based at least in part on the current condition, using the database, wherein the future condition is further based on the second change.

2. The inspection system of claim 1, wherein said database is generated offline using a phenomenological model.

3. The inspection system of claim 1, wherein:

the data points span a range of interest for each of the material condition and the time, and

each database data point also includes values for at least two properties to be estimated, the values being generated using a model.

4. The inspection system of claim 3, wherein the material condition is crack size, the time is measured in equivalent cycles, and one of the two values estimated at each data point is remaining life.

5. The inspection system of claim 1, wherein the current condition is described by a probability density function, and wherein the prediction module generates a distribution function describing, probabilistically, the future condition of the component.

6. The inspection system of claim 5, wherein the prediction module predicts the future condition of the component at a next scheduled inspection time, and the computing system further comprises:

a decision module that determines whether the future condition predicted at the next scheduled inspection time has a risk of failure that exceeds a damage tolerance limit.

7. The inspection system of claim 6, wherein if the decision module determines that the risk of failure at the next scheduled inspection exceeds the damage tolerance threshold limit, and in response provides an instruction to replace or repair the component.

8. The inspection system of claim 6, wherein if the decision module determines that the risk of failure at the next scheduled inspection exceeds the damage tolerance limit, then in response the decision module reschedules the next inspection time to an earlier time.

9. A non-transitory computer-readable storage medium comprising computer-executable instructions that, when executed by at least one processor, perform a method comprising acts of:

receiving at least three sets of sensor data, each of the at least three sets of sensor data comprising spatial data for a measured material condition of a component;

spatially registering the at least three sets of sensor data with respect to each other and the component;

computing a first change in the material condition of the component from the spatially registered at least three sets of sensor data;

estimating the current condition based at least in part on the first change in the material condition;

computing a second change in the material condition using the at least three sets of sensor data; and

predicting a future condition of the component at a future time based at least in part on the estimated current condition, wherein predicting the future condition is further based on the second change.

10. The non-transitory computer-readable storage medium of claim 9, wherein the future condition of the component is predicted using a database comprising a plurality of precomputed material conditions of the component, each precomputed material condition computed for a respective operating condition and time.

11. The non-transitory computer-readable storage medium of claim 10, wherein predicting the future condition comprises interpolating the future condition at the future time from the precomputed material conditions in the database.

12. The non-transitory computer-readable storage medium of claim 9, wherein predicting the future condition of the component comprises determining a distribution function describing, probabilistically, the future condition of the component at the future time.

13. The non-transitory computer-readable storage medium of claim 9, wherein the future time at which the future condition of the component is predicted is a next scheduled inspection time.

14. The non-transitory computer-readable storage medium of claim 13, wherein the method further comprises determining whether the future condition predicted at the next scheduled inspection time has a probability above a threshold that said future condition may exceed a damage tolerance limit and, if so, providing an instruction to replace or repair the component or to reschedule the next inspection time to an earlier time.

15. The non-transitory computer-readable storage medium of claim 9, wherein predicting the future condition comprises determining the future time as a time at which the future condition meets a replacement condition for the component.

16. The non-transitory computer-readable storage medium of claim 15, wherein the future time is measured in equivalent fatigue cycles.

17. The non-transitory computer-readable storage medium of claim 15, wherein the future condition is a predefined crack size limit.

18. The non-transitory computer-readable storage medium of claim 9, wherein the method further comprises an act of predicting a probability distribution for a future condition of the component using a probability distribution function for the current condition and a hyperlattice generated using a progression model for a process.

19. The non-transitory computer-readable storage medium of claim 18, wherein the process is crack initiation and growth.

20. The non-transitory computer-readable storage medium of claim 18, wherein the process is the evolution of a medical condition.

21. The non-transitory computer-readable storage medium of claim 18, wherein the process is a manufacturing process.

22. The non-transitory computer-readable storage medium of claim 18, wherein the process is a motion of a device.

23. The non-transitory computer-readable storage medium of claim 18, wherein the process is a machining process.