SYSTEM AND METHOD FOR PREDICTING ASSOCIATED FAILURE OF MACHINE COMPONENTS

• US 20160069778A1
• Filed: 09/10/2014
• Published: 03/10/2016
• Est. Priority Date: 09/10/2014
• Status: Abandoned Application

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: SAHU, Subrat
• Application Number:
  US14/482,631
• Publication Number:
  US 20160069778A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01M 17/00 Testing of vehicles testing fluid tightness G01M3/00; testing elastic properties of bodies or chassis, e.g. torsion-testing, G01M5/00; testing alignment of vehicle headlight devices G01M11/06; testing of engines G01M15/00
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance
• CPC Class Codes: G06Q 50/08 Construction

Abstract

A system for predicting failure of one or more components of a machine is disclosed. The system includes at least one interface configured for inputting current repair data for a first component, a database configured to log the current repair data of the first component, and a processor operably connected to the at least one interface and the database. The processor analyzes the current repair data of the first component based on historic repair data stored in the database, wherein the historic repair data includes the identity of a plurality of components of the machine, including the first component and a second component. The processor generates a recommendation for servicing the second component based on the historic repair data stored in the database.

Claims

1. A system for predicting failure of one or more components of a machine, the system comprising:

at least one interface configured for inputting current repair data for a first component;

a database configured to log the current repair data of the first component; and

a processor operably connected to the at least one interface and the database, wherein the processor;

analyzes the current repair data of the first component based on historic repair data stored in the database, wherein the historic repair data includes the identity of a plurality of components of the machine, including the first component and a second component; and

generates a recommendation for servicing the second component based on the historic repair data stored in the database.

2. The system of claim 1, wherein to analyze the current repair data, the processor:

applies an Apriori algorithm having a time limitation to determine whether one or more associated events occurred within the time limitation; and

applies a Pareto algorithm having a priority threshold to determine whether the one or more associated events meets the priority threshold.

3. The system of claim 2, wherein:

the database stores the costs of individual repair events, including repair events included in the one or more associated events;

the priority threshold of the Pareto algorithm is a percentage of total repair costs for the machine over a period of time; and

the Pareto algorithm determines which machine components of the historic repair data were involved in repair events totaling the percentage of total repair costs of the priority threshold.

4. The system of claim 3, wherein the Pareto algorithm computes the sum of costs of the individual repair events, starting with the most costly and continuing with the next most costly repair event until the combined cost equals at least the priority threshold.

5. The system of claim 2, wherein the Pareto algorithm includes a maximum limit on the number of associated events that meet the priority threshold.

6. The system of claim 2, wherein the processor applies the Apriori algorithm after the Pareto algorithm.

7. The system of claim 2, wherein the Apriori algorithm is used to determine a confidence percentage indicating the likelihood that the one or more associated events will occur within the time limitation.

8. The system of claim 1, wherein:

the database has stored thereon historic operating data of the operating conditions of the machine, wherein the operating data includes at least one of engine RPM, oil pressure, water temperature, boost pressure, oil contamination, electric motor current, hydraulic pressure, system voltage, payload, and tire performance; and

the processor analyzes the current repair data of the first component based on the historic operating data as part of generating the recommendation for servicing the second component.

9. A method of predicting failure of components of a machine, the method comprising:

inputting current repair data for a first component of the machine into a database;

processing the repair data, wherein the processing includes;

analyzing the current repair data of the first component based on historic repair data stored in the database, wherein the historic repair data includes the identity of a plurality of components of the machine, including the first component and a second component; and

generating a recommendation for servicing the second component based on the historic repair data stored in the database; and

outputting a recommended repair checklist.

10. The method of claim 9, wherein the recommended repair checklist is displayed on an electronic display.

11. The method of claim 10, wherein the display is located on-site of the machine.

12. The method of claim 9, wherein the analyzing includes:

applying an Apriori algorithm having a time limitation to determine whether one or more associated events occurred within the time limitation; and

applying a Pareto algorithm having a priority threshold to determine whether the one or more associated events meets the priority threshold.

13. The method of claim 12, wherein the Apriori algorithm is used to determine a confidence percentage indicating the likelihood that the one or more associated events will occur within the time limitation.

14. The method of claim 12, wherein

the database stores the costs of individual of repair events, including repair events included in the one or more associated events;

the priority threshold of the Pareto algorithm is a percentage of total repair costs for the machine over a period of time; and

the Pareto algorithm determines which machine components of the historic repair data were involved in repair events totaling the percentage of total repair costs of the priority threshold.

15. The method of claim 14, wherein the Pareto algorithm computes the sum of costs of the individual repair events, starting with the most costly and continuing with the next most costly repair event until the combined cost equals at least the priority threshold.

16. The method of claim 12, wherein the Pareto algorithm includes a maximum limit on the number of associated events that meet the priority threshold.

17. A computer-readable medium having stored thereon computer-readable instructions which, when executed by a processor, cause the processor to perform a method of predicting failure of one or more components of a machine, the method comprising:

inputting current repair data for a first component of the machine into a database;

processing the repair data, wherein the processing includes;

analyzing the current repair data of the first component based on historic repair data stored in the database, wherein the historic repair data includes the identity of a plurality of components of the machine, including the first component and a second component; and

generating a recommendation for servicing the second component based on the historic repair data stored in the database; and

outputting a recommended repair checklist.

18. The computer-readable medium of claim 17, wherein the recommended repair checklist is displayed on an electronic display.

19. The computer-readable medium of claim 17, wherein the analyzing includes:

applying an Apriori algorithm having a time limitation to determine whether one or more associated events occurred within the time limitation; and

applying a Pareto algorithm having a priority threshold to determine whether the one or more associated events meets the priority threshold.

20. The computer-readable medium of claim 19, wherein the Apriori algorithm is used to determine a confidence percentage indicating the likelihood that the one or more associated events will occur within the time limitation.