Bootstrap data methodology for sequential hybrid model building

• US 7,277,838 B2
• Filed: 08/26/2004
• Issued: 10/02/2007
• Est. Priority Date: 08/26/2004
• Status: Active Grant

• Current Assignee: Rtx Corporation
• Original Assignee: United Technologies Corporation (Rtx Corporation)
• Inventors: Volponi, Allan J., Brotherton, Thomas
• Primary Examiner(s): FERRIS III, FRED O
• Application Number:
  US10/926,760
• Publication Number:
  US 20060047487A1
• Time in Patent Office: 1,132 Days
• Field of Search: 703/2, 703/6, 703/7, 702/184, 702/189, 702/185, 701/100
• US Class Current: 703/7
• CPC Class Codes: G05B 17/02 electric

Abstract

A method for modeling engine operation comprising the steps of: 1. collecting a first plurality of sensory data, 2. partitioning a flight envelope into a plurality of sub-regions, 3. assigning the first plurality of sensory data into the plurality of sub-regions, 4. generating an empirical model of at least one of the plurality of sub-regions, 5. generating a statistical summary model for at least one of the plurality of sub-regions, 6. collecting an additional plurality of sensory data, 7. partitioning the second plurality of sensory data into the plurality of sub-regions, 8. generating a plurality of pseudo-data using the empirical model, and 9. concatenating the plurality of pseudo-data and the additional plurality of sensory data to generate an updated empirical model and an updated statistical summary model for at least one of the plurality of sub-regions.

Claims

1. A method for modeling engine operation comprising the steps of:

1. inputting a first plurality of sensory data from a gas turbine engine into a computer;

2. partitioning a flight envelope into a plurality of sub-regions according to the steps of;

3. selecting a first sensory parameter and a second sensory parameter,

4. plotting each of said first plurality of sensory data by using said first sensory parameter as a first axis and said second sensory parameter as a second axis, and

5. dividing said first axis and said second axis into a plurality of subdivisions to create a grid comprising a plurality of sub-regions;

6. assigning said first plurality of sensory data into said plurality of sub-regions;

7. generating an empirical model of at least one of said plurality of sub-regions;

8. generating a statistical summary model for at least one of said plurality of sub-regions;

9. inputting an additional plurality of sensory data from said gas turbine engine into said computer;

10. partitioning said second plurality of sensory data into said plurality of sub-regions;

11. generating a plurality of pseudo-data using said empirical model;

12. concatenating said plurality of pseudo-data and said additional plurality of sensory data; and

13. outputting using said computer an updated empirical model and an updated statistical summary model for at least one of said plurality of sub-regions.

2. The method of claim 1 comprising the additional step of repeating steps 8 through 11 until an updated empirical model and an updated statistical summary model is generated for each of said plurality of sub-regions.

3. The method of claim 1 wherein said selecting said first sensory parameter and said second sensory parameter comprises selecting ambient pressure and Reynold'"'"'s Index.

4. The method of claim 1 wherein said generating said empirical model comprises generating a multi-level perceptron artificial neural network (MLP ANN).

5. The method of claim 1 wherein said generating said empirical model comprises concatenating a plurality of said empirical models each corresponding to one of said plurality of sub-regions.

6. The method of claim 1 wherein said generating said statistical summary model comprises generating a radial basis function (RBF) ANN.

7. The method of claim 1 wherein inputting said plurality of sensory data comprises collecting a plurality of residuals each formed from the difference between an engine measurement and an output of a physical model of said engine.

12. The method of claim 1 wherein inputting said plurality of sensory data comprises collecting a plurality of residuals each formed from the difference between an engine measurement and an output of a physical model of said engine.

8. A method for modeling engine operation comprising the steps of:

inputting a first plurality of sensory data from a gas turbine engine into a computer;

partitioning a flight envelope into a plurality of sub-regions according to the steps of;

selecting a first sensory parameter and a second sensory parameter,

plotting each of said first plurality of sensory data by using said first sensory parameter as a first axis and said second sensory parameter as a second axis, and

dividing said first axis and said second axis into a plurality of subdivisions to create a grid comprising a plurality of sub-regions;

assigning said first plurality of sensory data into said plurality of sub-regions;

generating an empirical model of a portion of said plurality of sensory data;

generating a statistical summary model for said portion of said plurality of sensory data;

inputting an additional plurality of sensory data from said gas turbine engine into said computer;

generating a plurality of pseudo-data using said empirical model;

concatenating said plurality of pseudo-data and said additional plurality of sensory data; and

outputting using said computer an updated empirical model and an updated statistical summary model for at least a portion of said sensory data.

9. The method of claim 8 wherein said inputting said sensory data comprises collecting sensory data from a gas turbine engine.

10. The method of claim 8 wherein said generating said empirical model comprises generating a multi-level perceptron artificial neural network (MLP ANN).

11. The method of claim 8 wherein said generating said statistical summary model comprises generating a radial basis function (RBF) ANN.

13. An apparatus for modeling engine operation comprising:

means for inputting a first plurality of sensory data from a gas turbine engine into a computer;

means for partitioning said first plurality of sensory data into a plurality of sub-regions, said means for partitioning comprising the following;

means for selecting a first sensory parameter and a second sensory parameter,

means for plotting each of said first plurality of sensory data by using said first sensory parameter as a first axis and said second sensory parameter as a second axis, and

means for dividing said first axis and said second axis into a plurality of subdivisions to create a grid comprising a plurality of sub-regions;

means for generating an empirical model of at least one of said plurality of sub-regions;

means for generating a statistical summary model for at least one of said plurality of sub-regions;

means for inputting an additional plurality of sensory data from said gas turbine engine into said computer;

means for partitioning said second plurality of sensory data into said plurality of sub regions;

means for generating a plurality of pseudo-data using said empirical model;

means for concatenating said plurality of pseudo-data and said additional plurality of sensory data; and

outputting using said computer an updated empirical model and an updated statistical summary model for at least one of said plurality of sub-regions.

14. A method of constructing an empirical model, comprising the steps of:

1. inputting a first plurality of sensory data from a gas turbine engine into a computer;

2. partitioning an operating envelope into a plurality of sub-regions according to the steps of;

3. selecting a first sensory parameter and a second sensory parameter,

4. plotting each of said first plurality of sensory data by using said first sensory parameter as a first axis and said second sensory parameter as a second axis, and

5. dividing said first axis and said second axis into a plurality of subdivisions to create a grid comprising a plurality of sub-regions;

6. assigning said first plurality of sensory data into said plurality of sub-regions;

7. generating an empirical model of at least one of said plurality of sub-regions;

8. generating a statistical summary model for at least one of said plurality of sub-regions;

9. inputting an additional plurality of sensory data from said gas turbine engine into said computer;

10. partitioning said second plurality of sensory data into said plurality of sub-regions;

11. generating a plurality of pseudo-data using said empirical model;

12. concatenating said plurality of pseudo-data and said additional plurality of sensory data; and

13. outputting using a computer an updated empirical model and an updated statistical summary model for at least one of said plurality of sub-regions.