Method of forecasting maintenance of a machine

• US 20090037206A1
• Filed: 07/31/2007
• Published: 02/05/2009
• Est. Priority Date: 07/31/2007
• Status: Abandoned Application

• Current Assignee: Solar Turbines Incorporated (Caterpillar Incorporated)
• Original Assignee: Solar Turbines Incorporated (Caterpillar Incorporated)
• Inventors: Anderson, Chad Jerell, Swiniarski, Charles J., Byrne, Brian Dara
• Application Number:
  US11/882,237
• Publication Number:
  US 20090037206A1
• Time in Patent Office: Days
• US Class Current: 705/305
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance

Abstract

A method of forecasting maintenance of a machine is disclosed. The method includes measuring a parameter of the machine, the parameter being indicative of a condition of the machine, and transferring the measured parameter to a maintenance planning system. The method also includes predicting two or more parameter variation curves indicating the variation of the parameter over time, each parameter variation curve representing values of the parameter at a different confidence level. The method further includes identifying a first time period for maintenance of the machine based on the two or more parameter variation curves.

Claims

1. A method of forecasting maintenance of a machine, comprising:

measuring a parameter of the machine, the parameter being indicative of a condition of the machine;

transferring the measured parameter to a maintenance planning system;

predicting two or more parameter variation curves indicating the variation of the parameter over time, each parameter variation curve representing values of the parameter at a different confidence level; and

identifying a first time period for maintenance of the machine based on the two or more parameter variation curves.

2. The method of claim 1, wherein the first time period is a period of time from when one parameter variation curve reaches a threshold value to when another parameter variation curve reaches the threshold value, the threshold value being a value of the parameter indicative of a condition requiring maintenance of the machine.

3. The method of claim 2, further including performing maintenance of the machine during the first time period.

4. The method of claim 1, wherein the machine includes a plurality of machines located at a work site and the first time period is a period of time when at least one parameter variation curve of each machine of the plurality of machines is equal to or above a threshold value of the parameter, the threshold value being a value of the parameter indicative of a condition requiring maintenance of the machine.

5. The method of claim 1, further including;

measuring a second parameter of a second machine, the second parameter being indicative of a condition of the second machine;

predicting two or more second parameter variation curves indicating the variation of the second parameter over time, each second parameter variation curve representing values of the second parameter at a different confidence level;

identifying a second time period based on the two or more second parameter variation curves; and

identifying an overlapping time period, the overlapping time period being a period of time where both the first time period and the second time period overlap.

6. The method of claim 5, wherein;

the first time period is the period of time from when one parameter variation curve reaches a threshold value to when another parameter variation curve reaches the threshold value, the threshold value being a value of the parameter indicative of a condition requiring maintenance of the machine; and

the second time period is the period of time from when one second parameter variation curve reaches a second threshold value to when another second parameter variation curve reaches the second threshold value, the second threshold value being a value of the second parameter indicative of a condition requiring maintenance of the second machine.

7. The method of claim 6, further including performing maintenance of both machines during the overlapping time period.

8. The method of claim 1, wherein the parameter is measured using one or more sensors located on the machine.

9. The method of claim 1, wherein predicting two or more parameter variation curves includes predicting the parameter variation curves using at least one of analytical models, empirical models, or numerical models.

10. The method of claim 1, wherein transferring the measured parameter includes transferring the measured parameter to a remotely located maintenance planning system.

11. The method of claim 1, further including scheduling the maintenance of the machine in logistical planning systems, the logistical planning systems including one or more of an inventory management system or a personnel management system.

12. The method of claim 1, further including periodically updating the two or more parameter variation curves based on an updated value of the measured parameter.

13. The method of claim 12, further including periodically updating the first time period based on the updated two or more parameter variation curves.

14. A method of scheduling maintenance of a group of machines, comprising:

forecasting two or more failure times for each machine of the group of machines based on a measured parameter of the machine;

identifying a time period between the two or more failure times for each machine;

identifying a second time period as the period of time where the time periods of two or more machines of the group of machines overlap;

scheduling maintenance of the two or more machines during the second time period.

15. The method of claim 14, wherein forecasting two or more failure times includes determining the two or more failure time based on preexisting data.

16. The method of claim 14, wherein forecasting two or more failure times for each machine includes determining two or more times when failure of the machine are likely to occur based on probability.

17. The method of claim 14, wherein scheduling maintenance includes scheduling the maintenance in an inventory management system and a personnel management system.

18. A maintenance forecasting system for a group of machines comprising;

a sensor located on each machine of the group of machines, the sensor being configured to measure a parameter indicative of a condition of the machine;

a control system receiving the parameter from each machine of the group of machines, the control system being configured to analyze the parameter and display results, the results including,

predicted time periods of failure for each machine of the group of machines, the predicted time period being a period of time when failure of the machine may occur; and

a recommended maintenance time period, the recommended maintenance time period being a period of time when the predicted time periods of two or machines of the group of machines overlap.

19. The maintenance forecasting system of claim 18, wherein the parameter is transferred wirelessly to the control system and the control system is located remote from the group of machines.

20. The maintenance forecasting system of claim 18, wherein the group of machines includes a group of gas turbine engines.