Apparatus for predicting life of rotary machine and equipment using the same

• US 20040143418A1
• Filed: 01/08/2004
• Published: 07/22/2004
• Est. Priority Date: 03/23/2001
• Status: Active Grant

• Current Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: Nakado, Takashi, Arikado, Tsunetoshi, Samata, Shuichi, Ushiku, Yukihiro, Mikata, Yuuichi
• Granted Patent: US 6,944,572 B2
• Time in Patent Office: Days
• US Class Current: 702/184
• CPC Class Codes: F04B 49/065 and making use of computers
• CPC Class Codes: F04B 51/00 Testing machines, pumps, or pumping installations
• CPC Class Codes: F04D 19/04 specially adapted to the production of a high vacuum, e.g. molecular pumps
• CPC Class Codes: F04D 27/001 Testing thereof; Determination or simulation of flow characteristics; Stall or surge detection, e.g. condition monitoring
• CPC Class Codes: G01M 13/00 Testing of machine parts

Abstract

An apparatus for predicting life expectancy of a rotary machine includes: a load recipe input module acquiring loading conditions of a rotary machine; a characterizing feature input module obtaining characterizing feature data of a rotary machine; and a life expectancy prediction module calculating life expectancy of the rotary machine in conformity with the loading conditions and the characterizing feature data.

Claims

1. An apparatus for predicting life of rotary machine comprising

a load recipe input module configured to acquire loading conditions of a rotary machine;

a characterizing feature input module configured to obtain characterizing feature data of said rotary machine; and

a life expectancy prediction module configured to calculate life expectancy of said rotary machine in conformity with said loading conditions and said characterizing feature data.

2. The apparatus of claim 1, wherein said loading conditions are defined by process conditions of a chamber where gas is introduced and a semiconductor manufacturing process is performed, and said rotary machine is a vacuum pump.

3. The apparatus of claim 2, wherein said process conditions include kind of said gas and flow rate of said gas.

4. The apparatus of claim 2, wherein said characterizing feature data include one of voltage, current, power, temperature, vibration, and sound of said vacuum pump.

5. The apparatus of claim 2, wherein calculation of said life expectancy uses time series data of said characterizing feature data.

6. The apparatus of claim 5, wherein calculation of said life expectancy uses any one of average value, standard deviation, auto correlation coefficient, lag width of an auto correlation coefficient, and statistical analysis value of a Mahalanobis-Taguchi distance of a limited time segment in said time series data for characterizing feature data.

7. The apparatus of claim 5, wherein said calculation of life expectancy is derived from speed of change of said statistical analysis value, a simple regression or multiple regression analysis.

8. The apparatus of claim 2, wherein said calculated life expectancy is fed to a local area network.

9. The apparatus of claim 2, wherein based on said calculated life expectancy, an emergency shutoff signal for said process is provided.

10. Manufacturing equipment comprising:

a process controller configured to control a production process;

a rotary machine configured to process load of said production process; and

a life expectancy prediction controller configured to calculate life expectancy of said rotary machine in conformity with process recipe obtained by said process controller and characterizing feature data obtained from said rotary machine.

11. The manufacturing equipment of claim 10, wherein said process recipe defines process conditions of a chamber where gas is introduced and a semiconductor manufacturing process is performed, and said rotary machine is a vacuum pump.

12. The manufacturing equipment of claim 10, wherein said life expectancy prediction controller is supplied with a plurality of process recipes.

13. The manufacturing equipment of claim 11, wherein said process conditions include kind of said gas and flow rate of said gas.

14. The manufacturing equipment of claim 0.11, wherein said characterizing feature data include one of voltage, current, power, temperature, vibration, and sound of said vacuum pump.

15. The manufacturing equipment of claim 11, wherein calculation of said life expectancy uses time series data of said characterizing feature data.

16. The manufacturing equipment of claim 15, wherein said calculation of said life expectancy uses any one of average value, standard deviation, auto correlation coefficient, lag width of an auto correlation coefficient, and statistical analysis value of a Mahalanobis-Taguchi distance of a limited time segment in said time series data for said characterizing feature data.

17. The manufacturing equipment of claim 15, wherein said calculation of life expectancy is derived from speed of change of said statistical analysis value, a simple regression or multiple regression analysis.

18. The manufacturing equipment of claim 11, wherein said calculated life expectancy is fed to a local area network.

19. The manufacturing equipment of claim 11, wherein based on said calculated life expectancy, an emergency shutoff signal for said process is provided.

20. The manufacturing equipment of claim 11, wherein said process controller and said life expectancy prediction controller are installed in a same case.

21. A method for predicting life of rotary machine comprising:

reading a load recipe of loading conditions of a rotary machine;

determining whether changes exist for said loading conditions by comparing said load recipe with an already existing load recipe for the process;

employing an already existing determination reference if no changes exist for said loading conditions, and reading in and employing a determination reference accommodating said process conditions if changes exist for said loading conditions instead of said already existing determination reference;

processing time series data by reading in detected characterizing feature data for said rotary machine, which correspond to said determination reference; and

calculating life expectancy of said rotary machine in conformity with said time series data and said determination reference.

22. The method of claim 21, wherein said loading conditions are defined by process conditions of a chamber where gas is introduced and a semiconductor manufacturing process is performed, and said rotary machine is a vacuum pump.

23. The method of claim 22, wherein another semiconductor manufacturing process having different object from said semiconductor manufacturing process is further performed in said chamber.

24. A method of claim 22, wherein said process conditions include kind of said gas and flow rate of said gas.

25. The method of claim 22, wherein said characterizing feature data include one of voltage, current, power, temperature, vibration, and sound of said vacuum pump.

26. The method of claim 22, wherein calculation of said life expectancy uses time series data of said characterizing feature data.

27. The method of claim 26, wherein calculation of said life expectancy uses any one of average value, standard deviation, auto correlation coefficient, lag width of an auto correlation coefficient, and statistical analysis value of a Mahalanobis-Taguchi distance of a limited time segment in said time series data for said characterizing feature data.

28. The method of claim 26, wherein said calculation of life expectancy is derived from speed of change of said statistical analysis value, a simple regression or multiple regression analysis.

29. The method of claim 22, wherein said calculated life expectancy is fed to a local area network.

30. The method of claim 22, wherein based on said calculated life expectancy, an emergency shutoff signal for said process is provided.

31. A method for determining timing of rotary machine repair comprising:

reading a load recipe of loading conditions of a rotary machine;

determining whether changes exist for said loading conditions by comparing said load recipe with an already existing load recipe for the process;

employing an already existing determination reference if no changes exist for said loading conditions, and reading in and employing a determination reference accommodating said process conditions if changes exist for said loading conditions instead of said already existing determination reference;

processing time series data by reading in detected characterizing feature data for said rotary machine, which correspond to said determination reference;

calculating life expectancy of said rotary machine in conformity with said time series data and said determination reference;

finding stand-by times of the process in a time period until said calculated life expectancy is reached, by a semiconductor production simulator; and

.

determining a stand-by time, of said found stand-by times, which least affects said process or a time including said stand-by time, to be the replacement or repair time of said rotary machine.

32. The method of claim 31, wherein said loading conditions are defined by process conditions of a chamber where gas is introduced and a semiconductor manufacturing process is performed, and said rotary machine is a vacuum pump.

33. The method of claim 32, wherein another semiconductor manufacturing process having different object from said semiconductor manufacturing process is further performed in said chamber.

34. The method of claim 32, wherein said process conditions include kind of said gas and flow rate of said gas.

35. The method of claim 32, wherein said characterizing feature data include one of voltage, current, power, temperature, vibration, and sound of said vacuum pump.

36. The method of claim 32, wherein calculation of said life expectancy uses time series data of said characterizing feature data.

37. The method of claim 36, wherein calculation of said life expectancy uses any one of average value, standard deviation, auto correlation coefficient, lag width of an auto correlation coefficient, and statistical analysis value of a Mahalanobis-Taguchi distance of a limited time segment in said time series data for said characterizing feature data.

38. The method of claim 36, wherein said calculation of life expectancy is derived from speed of change of said statistical analysis value, a simple regression or multiple regression analysis.