Method for improving severity estimates

Patent metadata

Field	Value
Publication number	US-10288530-B2
Application number	US-201514727022-A
Country	US
Kind code	B2
Filing date	Jun 1, 2015
Priority date	Jun 1, 2015
Publication date	May 14, 2019
Grant date	May 14, 2019

Abstract

A method for providing improved composite work cycle damage estimates includes constructing a damage rate basis matrix, performing a D-optimal row selection calculation on the damage rate basis matrix, selecting, based on the D-optimal row selection calculation, a finite number of strain measurement device locations on the machine, extracting a target percentile damage rate for each of the one or more strain measurement devices, and using the extracted damage rates to solve for the unknown coefficients and verify the weightings assigned to the machine operations.

First claim

What is claimed is:

1. A method for performing a work operation with a machine based on a composite work cycle damage rate estimate, the method comprising:
constructing, by one or more processors of a computer system, a damage rate basis matrix, wherein:
each row of the damage rate basis matrix corresponds to a location on a machine; and
each column of the damage rate basis matrix corresponds to a test event in a composite work cycle;

performing, by the one or more processors of the computer system, a D-optimal row selection calculation on the damage rate basis matrix to maximize a determinant of the damage rate basis matrix;
assigning the work cycle damage rate estimate to an operation of the machine;
selecting, based on the D-optimal row selection calculation, a finite number of strain measurement device locations on the machine;
placing, on the machine, a strain measurement device at each of the selected strain measurement device locations;
calculating, by the one or more processors of the computer system, a vector of coefficients that, when multiplied by the damage rate basis matrix, results in the extracted target percentile damage rates for each of the one or more strain measurement devices;
verifying, based on a calculated vector of coefficients, weightings assigned to operations of the machine;
transferring, via the one or more processors, the composite work cycle damage rate estimate assigned to an operation of the machine, to one or more on-board modules of the machine; and
performing the operation with the machine, using the one or more on-board modules, based on the composite work cycle damage rate estimate.

2. The method of claim 1 , wherein:
calculating the vector of coefficients includes performing a pseudo-inverse calculation; and
the vector of coefficients corresponds corresponding to a simulated event.

3. The method of claim 1 , wherein:
selecting the finite number of strain measurement device locations on the machine includes selecting optimum strain measurement device locations to calculate the unknown coefficients for a damage test event.

4. The method of claim 3 , further comprising:
identifying a number of strain measurement devices corresponding to a point of diminishing returns.

5. The method of claim 4 , further comprising:
diagnosing multi-collinearity prior to designating strain measurement device placement locations on a machine.

6. The method of claim 1 , further comprising:
detecting one or more redundant test events in a composite work cycle.

7. The method of claim 6 , wherein detecting the one or more redundant test events in the composite work cycle includes:
determining if a damage rate calculation is a linear combination of two or more other damage rate calculations in the composite work cycle.

8. The method of claim 1 , wherein calculating the vector of coefficients comprises performing a least-squares pseudo-inverse calculation.

9. The method of claim 1 , wherein the target percentile damage rates for each of the one or more strain measurement devices are represented as a damage rate column vector, the method further comprising:
analyzing at least one of a delta between at least two test events or a residual vector created from collected test events;
based on the analyzing, determining that one or more test events are missing from the composite work cycle; and
based on the determining, creating a reconstituted damage rate column vector.

10. The method of claim 1 , further comprising:
using at least one standard and at least one contrived event as inputs in the D-optimal row selection calculation.

11. One or more non-tangible computer-readable media comprising computer-executable instructions that, when executed on a processor, cause a computing system to perform operations for performing a work operation with a machine based on a composite work cycle damage rate estimate, the operations comprising:
constructing a damage rate basis matrix, wherein:
each row of the damage rate basis matrix corresponds to a location on a machine; and
each column of the damage rate basis matrix corresponds to a test event in a composite work cycle;

performing a D-optimal row selection calculation on the damage rate basis matrix to maximize a determinant of the damage rate basis matrix;
assigning the work cycle damage rate estimate to an operation of the machine;
selecting, based on the D-optimal row selection calculation, a finite number of strain measurement device locations on the machine;
extracting a target percentile damage rate for each of the one or more strain measurement devices;
calculating a vector of coefficients that, when multiplied by the damage rate basis matrix, results in the extracted target percentile damage rates for each of the one or more strain measurement devices;
verifying, based on the calculated vector of coefficients, weightings assigned to operations of the machine operations;
transferring, via one or more processors, the composite work cycle damage rate estimate assigned to the operation of the machine, to one or more on-board modules of the machine; and
performing the operation with the machine, using the one or more on-board modules, based on the composite work cycle damage rate estimate.

12. The one or more non-tangible computer-readable media of claim 11 , wherein:
calculating the vector of coefficient includes performing a pseudo-inverse calculation; and
the vector of coefficients corresponds to a simulated event.

13. The one or more non-tangible computer-readable media of claim 11 , wherein:
selecting the finite number of strain measurement device locations on the machine includes selecting optimum strain measurement device locations to calculate the unknown coefficients for a damage test event.

14. The one or more non-tangible computer-readable media of claim 13 , wherein selecting the finite number of strain measurement device locations on the machine further includes:
identifying a number of strain measurement devices corresponding to a point of diminishing returns.

15. The one or more non-tangible computer-readable media of claim 14 , the operations further comprising:
diagnosing multi-collinearity prior to designating strain measurement device placement locations on a machine.

16. The one or more non-tangible computer-readable media of claim 11 , the operations further comprising:
detecting one or more redundant test events in a composite work cycle.

17. The one or more non-tangible computer-readable media of claim 16 , wherein detecting the one or more redundant test events in the composite work cycle includes:
determining if a damage rate calculation is a linear combination of two or more other damage rate calculations in the composite work cycle.

18. The one or more non-tangible computer-readable media of claim 11 , wherein calculating the vector of coefficients comprises performing a least-squares pseudo-inverse calculation.

19. The one or more non-tangible computer-readable media of claim 11 , wherein the target percentile damage rates for each of the one or more strain measurement devices are represented as a damage rate column vector, the operations further comprising:
analyzing at least one of a delta between at least two test events or a residual vector created from collected test events;
based on the analyzing, determining that one or more test events are missing from the composite work cycle; and
based on the determining, creating a reconstituted damage rate column vector.

Assignees

• Caterpillar Inc

Inventors

• Sundermeyer Jeffry N
• Hodel Benjamin
• Wieland Nathan J
• Nygaard Jim A

Classifications

• G01N2203/0073

  Fatigue · CPC title

• G05B23/0283

  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 title

• G06Q10/20

  Administration of product repair or maintenance · CPC title

• G01M99/005 Primary

  Testing of complete machines, e.g. washing-machines or mobile phones (testing of machine parts G01M13/00; testing of electric apparatus or components G01R31/50) · CPC title

• G05B23/024

  Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks · CPC title