Caterpillar Inc.

Jeffry Neil Sundermeyer

Timothy A Vik

Ryan Allgaier

Burton Roland Clarke

Daniel Kimsey Dunn, Iii

Jeffrey Dale Baskett

Nitin R Patel

Don Sit

Hiroko Kyuba

Byron Edwin Truax

Benjamin J Hodel

James A Forck

Nathan J Wieland

Jim A Nygaard

Damage rate data associated with a plurality of positions on a machine can be used construct a damage rate covariance matrix, from which a matrix of modes can be derived. A subset of modes from the matrix of modes can be identified, and damage rate monitoring positions on the machine can be selected by identifying a subset of the plurality of positions on the machine that maximize a determinant associated with the matrix of modes using D-optimal row selection.

Modal analysis for damage rate measurement and prediction

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.

Method for improving severity estimates

Methods and systems for constructing a load history database for a structure is disclosed. In one embodiment, a method is disclosed that may include detecting a measurable parameter on the structure utilizing a sensor positioned on the structure and determining a value of external loads acting upon the structure based on the detected parameter. Further, the method may include evaluating the value of the external loads against a pre-established factor. Based on the evaluation, the value of the external loads are selectively stored in the load history database.

Systems and methods for maintaining load histories

A monitoring system is provided, which may include a structural component configured to undergo mechanical loading and a wireless node attached to the structural component. The node may include a strain sensing device configured to measure strain experienced by the structural component at the location of the node. The node may also include a processor configured to predict, based on the strain measurements, fatigue life of the structural component.

Monitoring system

A method for analyzing the use of a work machine is disclosed. In one embodiment, the method may include providing a computer with a neural network on the work machine. Further, the method may include inputting data to the computer, at least a portion of the data associated with a load experienced by one of the components of the work machine. The neural network, when executed by the computer may then classify a current operation of the work machine into one of a plurality of types of operations.

Classifying a work machine operation

A strain sensing device is provided, which may include a hollow, cylindrical member having an inner surface and an outer surface. The outer surface may have an outer diameter corresponding approximately in size to an inner diameter of an inner surface of a cylindrical bore of a structural component configured to undergo mechanical loading. The hollow, cylindrical member may be configured to mate to the inner surface of the cylindrical bore of the structural component such that strain in the structural component is translated into strain in the hollow, cylindrical member. The strain sensing device may also include one or more strain sensing elements attached to the inner surface of the hollow, cylindrical member and configured to detect strain exhibited by the hollow, cylindrical member.

Strain sensing device

A method and method of estimating fatigue life of a structure is disclosed. In one embodiment, the method may include determining strain on the structure using a plurality of gauges positioned on the structure. The method may also include detecting a parameter of the structure with a plurality of sensors positioned on the structure. Further, the method includes converting the detected parameter and the determined strain to proportional loads acting on the structure and transforming the proportional loads to coordinate system data associated with the structure. Based on the coordinate system data, the fatigue life of the structure may be estimated.

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Dunlap, IL, United States of America

Jeffry Neil Sundermeyer