DATA DRIVEN METHOD AND SYSTEM FOR PREDICTING OPERATIONAL STATES OF MECHANICAL SYSTEMS

• US 20100082267A1
• Filed: 09/30/2008
• Published: 04/01/2010
• Est. Priority Date: 09/30/2008
• Status: Active Grant

• Current Assignee: The Boeing Co.
• Original Assignee: The Boeing Co.
• Inventors: Basu, Sabyasachi, Farner, Scott, Wineland, Art, Schimert, James
• Granted Patent: US 8,165,826 B2
• Time in Patent Office: Days
• US Class Current: 702/34
• CPC Class Codes: G05B 19/4065 Monitoring tool breakage, life or condition
• CPC Class Codes: G05B 2219/37252 Life of tool, service life, decay, wear estimation
• CPC Class Codes: G07C 3/00 Registering or indicating the condition or the working of machines or other apparatus, other than vehicles

Abstract

There is provided an automated data driven method for predicting one or more operational states, such as wear or degradation, of a mechanical system over time. The method comprises the steps of collecting data on the mechanical system from a data recording device, preprocessing the collected data, selecting a training data set that represents a base condition for statistical comparison, fitting a statistical model to the training data set to relate a predicted response to nuisance variables at the base condition, and using an output model to predict what an observed response would have been at the base condition and calculating the difference between the observed response and the predicted response to predict the one or more operational states of the mechanical system.

Claims

1. An automated data driven method for predicting one or more operational states of a mechanical system over time, the method comprising the steps of:

collecting data on the mechanical system from a data recording device;

preprocessing the collected data;

selecting a training data set that represents a base condition for statistical comparison;

fitting a statistical model to the training data set to relate a predicted response to nuisance variables at the base condition; and

,

using an output model to predict what an observed response would have been at the base condition and calculating the difference between the observed response and the predicted response to predict the one or more operational states of the mechanical system.

2. The method of claim 1 wherein the collecting data step further comprises the step of determining one or more technical parameters of the mechanical system to be measured relevant to the predicting of the one or more operational states.

3. The method of claim 1 wherein the method further comprises the step of using the predicted one or more operational states for trend analysis.

4. The method of claim 1 wherein the statistical model comprises a model selected from the group comprising linear regression, non-linear regression, and adaptive nonparametric procedures.

5. The method of claim 1 wherein the statistical model is a random forest technique.

6. The method of claim 1 wherein the mechanical system is selected from the group comprising engines, turbines, tires, and brakes.

7. The method of claim 1 wherein the mechanical system is in an apparatus selected from the group comprising aircraft, automobiles, trucks, watercraft, power generator units, and military vehicles.

8. The method of claim 1 wherein the operational state is selected from the group comprising wear, degradation, part failure, and anomalies.

9. The method of claim 1 wherein the method uses the statistical model to model aircraft gas turbine engine exhaust gas temperature (EGT) as a function of environmental, flight and engine parameters and to generate the output model as a predictive model of engine wear.

10. The method of claim 1 wherein the nuisance variables are selected from the group comprising environmental influences, flight conditions, system faults, and mechanical parameters.

11. An automated data driven method for predicting wear of a mechanical system over time, the method comprising the steps of:

collecting data on the mechanical system from a data recording device;

determining and measuring one or more technical parameters of the mechanical system relevant to the predicting of wear of the mechanical system;

preprocessing the collected data to summarize operation of the mechanical system;

selecting a training data set that represents a base condition for statistical comparison;

fitting a statistical model to the training data set to relate a predicted response to nuisance variables at the base condition;

using an output model to predict what an observed response would have been at the base condition and calculating the difference between the observed response and the predicted response to predict the wear of the mechanical system;

plotting the predicted wear; and

,

using the plotted wear for trend analysis.

12. The method of claim 11 wherein the statistical model comprises a model selected from the group comprising linear regression, non-linear regression, and adaptive nonparametric procedures.

13. The method of claim 11 wherein the statistical model is a random forest technique.

14. The method of claim 11 wherein the mechanical system is selected from the group comprising engines, turbines, tires, and brakes.

15. The method of claim 11 wherein the mechanical system is in an apparatus selected from the group comprising aircraft, automobiles, trucks, watercraft, power generator units, and military vehicles.

16. The method of claim 11 wherein the method uses the statistical model to model aircraft gas turbine engine exhaust gas temperature (EGT) as a function of environmental, flight and engine parameters and uses the output model to predict engine wear.

17. An automated data driven system for predicting one or more operational states of a mechanical system over time comprising:

a data collection component for collecting data on the mechanical system;

a preprocessing component for preprocessing the collected data;

a training data set selection component for selecting a training data set that represents a base condition for statistical comparison;

a statistical modeling component for fitting a statistical model to the training data set to relate a predicted response to nuisance variables at the base condition;

an output model component; and

,

a predicting component that uses the output model to predict what an observed response would have been at the base condition and calculates the difference between the observed response and the predicted response to predict the one or more operational states of the mechanical system in order to generate a predicted operational state component.

18. The system of claim 17 wherein the statistical modeling component is a random forest technique.

19. The system of claim 17 wherein the mechanical system is selected from the group comprising engines, turbines, tires, and brakes.

20. The system of claim 17 wherein the mechanical system is in an apparatus selected from the group comprising aircraft, automobiles, trucks, watercraft, power generator units, and military vehicles.