Manufacturing apparatus and method for predicting life of rotary machine used in the same

• US 20040064212A1
• Filed: 01/03/2003
• Published: 04/01/2004
• Est. Priority Date: 09/30/2002
• Status: Abandoned Application

• Current Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: Nakao, Takashi, Samata, Shuichi, Furuhata, Takeo, Ushiku, Yukihiro
• Application Number:
  US10/336,022
• Publication Number:
  US 20040064212A1
• Time in Patent Office: Days
• US Class Current: 700/108
• CPC Class Codes: F04C 18/18 with similar tooth forms F04C18/16 takes precedence
• CPC Class Codes: F04C 2220/12 Dry running
• CPC Class Codes: F04C 2220/30 Use in a chemical vapor deposition [CVD] process or in a similar process
• CPC Class Codes: F04C 2270/07 Electric current
• CPC Class Codes: F04C 2270/80 Diagnostics
• CPC Class Codes: F04C 28/28 Safety arrangements; Monitoring
• CPC Class Codes: F04C 29/0085 Prime movers

Abstract

A method for predicting life of a rotary machine used in a manufacturing apparatus, includes: determining a starting time of an abnormal condition just before a failure of a monitor rotary machine used in a monitor manufacturing process, from monitor time-series data for characteristics of the monitor rotary machine, statistically analyzing the monitor time-series data, and finding a value for the characteristics at the starting time of the abnormal condition as a threshold of the abnormal condition; measuring diagnosis time-series data for the characteristic of a motor current of a diagnosis rotary machine during a manufacturing process; preparing diagnosis data from the diagnosis time-series data; and determining a time for the diagnosis data exceeding the threshold as the life of the diagnosis rotary machine.

Claims

1. A method for predicting life of a rotary machine used in a manufacturing apparatus, comprising:

determining a starting time of an abnormal condition just before a failure of a monitor rotary machine used in a monitor manufacturing process, from monitor time-series data for characteristics of the monitor rotary machine, statistically analyzing the monitor time-series data, and finding a value for the characteristics at the starting time of the abnormal condition as a threshold of the abnormal condition;

measuring diagnosis time-series data for the characteristic of a motor current of a diagnosis rotary machine during a manufacturing process;

preparing diagnosis data from the diagnosis time-series data; and

determining a time for the diagnosis data exceeding the threshold as the life of the diagnosis rotary machine.

2. The method of claim 1, wherein the threshold is determined using a Mahalanobis distance obtained from the monitor time-series data.

3. The method of claim 2, wherein the characteristic of the motor current includes a number of current peaks generated in the manufacturing process.

4. The method of claim 3, wherein the diagnosis data is prepared using a plurality of the characteristics having a different error risk rate misdiagnosed as the abnormal condition due to the diagnosis data exceeding the threshold under a normal condition prior to entering the abnormal condition.

5. The method of claim 4, wherein variations in the motor current due to a power supply are selected by monitoring at least one of a motor voltage and a motor power of the diagnosis rotary machine.

6. The method of claim 1, wherein the characteristic of the motor current includes a number of current peaks generated in the manufacturing process.

7. The method of claim 6, wherein the diagnosis data is prepared using a plurality of the characteristics having a different error risk rate misdiagnosed as the abnormal condition due to the diagnosis data exceeding the threshold under a normal condition prior to entering the abnormal condition.

8. The method of claim 7, wherein variations in the motor current due to a power supply are selected by monitoring at least one of a motor voltage and a motor power of the diagnosis rotary machine.

9. The method of claim 1, wherein the diagnosis data is prepared using a plurality of the characteristics having a different error risk rate misdiagnosed as the abnormal condition due to the diagnosis data exceeding the threshold under a normal condition prior to entering the abnormal condition.

10. The method of claim 9, wherein variations in the motor current due to a power supply are selected by monitoring at least one of a motor voltage or a motor power of the diagnosis rotary machine.

11. The method of claim 1, wherein variations in the motor current due to a power supply are selected by monitoring at least one of a motor voltage and a motor power of the diagnosis rotary machine.

12. A manufacturing apparatus using a rotary machine, comprising:

a diagnosis rotary machine performing a manufacturing process;

a measurement unit configured to measure diagnosis time-series data for characteristics of a motor current of the diagnosis rotary machine during the manufacturing process; and

a data processing unit configured to prepare diagnosis data from the diagnosis time-series data, and determine a time for the diagnosis data exceeding the threshold found statistically from a monitor time-series data for characteristics of a monitor rotary machine, as a life of the diagnosis rotary machine.

13. The manufacturing apparatus of claim 12, wherein the measurement unit includes at least one of a voltmeter measuring a motor voltage and a wattmeter measuring a motor power, for the diagnosis rotary machine.

14. The manufacturing apparatus of claim 13, wherein the diagnosis rotary machine is a dry pump used in a semiconductor manufacturing apparatus.

15. The manufacturing apparatus of claim 14, wherein the data processing unit is provided to a computer on a local area network.

16. The manufacturing apparatus of claim 14, wherein the data processing unit is provided to a data processing system on a computer integrated manufacturing system.

17. The manufacturing apparatus of claim 12, wherein the diagnosis rotary machine is a dry pump used in a semiconductor manufacturing apparatus.

18. The manufacturing apparatus of claim 17, wherein the data processing unit is provided to a computer on a local area network.

19. The manufacturing apparatus of claim 12, wherein the data processing unit is provided to a computer on a local area network.

20. The manufacturing apparatus of claim 12, wherein the data processing unit is provided to a data processing system upon a computer integrated manufacturing system.