Method for improving severity estimates

Patent metadata

Field	Value
Publication number	US-2016349151-A1
Application number	US-201514727022-A
Country	US
Kind code	A1
Filing date	Jun 1, 2015
Priority date	Jun 1, 2015
Publication date	Dec 1, 2016
Grant date	—

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 providing improved composite work cycle damage rate estimates comprising:
constructing a damage rate basis matrix, the damage rate basis matrix configured to be multiplied by a plurality of unknown coefficients representing weightings assigned to machine operations; 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, wherein the finite number of strain measurement device locations optimizes use of one or more strain measurement devices to be placed 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.

2 . The method of claim 1 , wherein a pseudo-inverse calculation is used to determine a range of unknown coefficients corresponding to a simulated event.

3 . The method of claim 1 , further comprising:
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 , further comprising:
determining a relative ratio of one or more events in the composite work cycle; and providing an overall severity multiplier that provides optimal matching with observed target percentile field damage rates.

9 . The method of claim 1 , further comprising:
detecting one or more missing events in the composite work cycle by analyzing at least one of a delta between at least two test events or a residual vector created from collected test events.

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 . A computer-readable medium is provided. A processor may be configured to execute instructions stored on a computer-readable medium to perform a method including:
constructing a damage rate basis matrix, the damage rate basis matrix configured to be multiplied by a plurality of unknown coefficients representing weightings assigned to machine operations; 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, wherein the finite number of strain measurement device locations optimizes use of one or more strain measurement devices to be placed 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.

12 . The computer-readable medium of claim 11 , wherein a pseudo-inverse calculation is used to determine a range of unknown coefficients corresponding to a simulated event.

13 . The computer-readable medium of claim 11 , further comprising:
selecting optimum strain measurement device locations to calculate the unknown coefficients for a damage test event.

14 . The computer-readable medium of claim 13 , further comprising:
identifying a number of strain measurement devices corresponding to a point of diminishing returns.

15 . The computer-readable medium of claim 14 , further comprising:
diagnosing multi-collinearity prior to designating strain measurement device placement locations on a machine.

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

17 . The computer-readable medium 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 computer-readable medium of claim 11 , further comprising:
determining a relative ratio of one or more events in the composite work cycle; and providing an overall severity multiplier that provides optimal matching with observed target percentile field damage rates.

19 . The computer-readable medium of claim 1 , further comprising:
detecting one or more missing events in the composite work cycle by analyzing at least one of a delta between at least two test events or a residual vector created from collected test events.

20 . A system providing improved composite work cycle damage rate estimates comprising:
an analysis module configured to: construct a damage rate basis matrix, the damage rate basis matrix configured to be multiplied by a plurality of unknown coefficients representing weightings assigned to machine operations; perform a D-optimal row selection calculation on the damage rate basis matrix; select, based on the D-optimal row selection calculation, a finite number of strain measurement device locations on the machine, wherein the finite number of strain measurement device locations optimizes use of one or more strain measurement devices to be placed on the machine; extract a target percentile damage rate for each of the one or more strain measurement devices; and solve for the unknown coefficients and verify the weightings assigned to the machine operations; and using at least one standard and at least one contrived event as inputs in the D-optimal row selection calculation.

Assignees

• Caterpillar Inc

Inventors

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

Classifications

• G06Q10/20

  Administration of product repair or maintenance · 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

• 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

• G01N2203/0073

  Fatigue · CPC title

• G01M5/0033

  by determining damage, crack or wear · CPC title