Manufacturing apparatus and method for predicting life of a manufacturing apparatus which uses a rotary machine

• US 20040064277A1
• Filed: 03/19/2003
• Published: 04/01/2004
• Est. Priority Date: 09/27/2002
• Status: Active Grant

• Current Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: Yamamoto, Akihito, Nakao, Takashi, Samata, Shuichi, Furuhata, Takeo, Ushiku, Yukihiro
• Granted Patent: US 7,065,469 B2
• Time in Patent Office: Days
• US Class Current: 702/76
• CPC Class Codes: G01M 15/12 by monitoring vibrations
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06

Abstract

A manufacturing apparatus which includes a rotary machine, includes: a plurality of accelerometers configured to measure diagnosis time series data attached to the rotary machine at locations where variations of the rotary machine are different; a frequency analysis device configured to perform a frequency analysis on the diagnosis time series data measured by the plurality of accelerometers; a time series data recording module configured to generate diagnosis data based on variations in characteristics of vibration corresponding to an analysis target frequency and to record the diagnosis data; and a life prediction unit configured to analyze the diagnosis data to determine a life span of the rotary machine.

Claims

1. A manufacturing apparatus which includes a rotary machine, comprising:

a plurality of accelerometers configured to measure diagnosis time series data attached to the rotary machine at locations where variations of the rotary machine are different;

a frequency analysis device configured to perform a frequency analysis on the diagnosis time series data measured by the plurality of accelerometers;

a time series data recording module configured to generate diagnosis data based on variations in characteristics of vibration corresponding to an analysis target frequency and to record the diagnosis data; and

a life prediction unit configured to analyze the diagnosis data to determine a life span of the rotary machine.

2. The manufacturing apparatus of claim 1, wherein the plurality of accelerometers are attached to a first surface of the rotary machine and a second surface of the rotary machine facing to the first surface.

3. The manufacturing apparatus of claim 1, wherein the plurality of accelerometers are attached to locations where phases of variations in a vibration of the rotary machine are different.

4. The manufacturing apparatus of claim 3, wherein the plurality of accelerometers are attached to locations near a center of the rotary machine and a end of the rotary machine.

5. The manufacturing apparatus of claim 1, wherein the analysis target frequency is an integral multiple of a normal frequency unique to the rotary machine.

6. The manufacturing apparatus of claim 1, wherein the analysis target frequency is expressed as a product of an equation including a number of rotor blades of the rotary machine and a normal frequency unique to the rotary machine.

7. The manufacturing apparatus of claim 6, wherein the equation including the number of blades is expressed as [n+(1/m)], where m is the number of the blades, and n, l are arbitrary positive integers.

8. The manufacturing apparatus of claim 1, wherein the life prediction unit includes an acceleration transition determination module using a peak acceleration as a characteristic of the diagnosis data.

9. The manufacturing apparatus of claim 1, wherein the life prediction unit includes a total acceleration transition determination module using a total acceleration integrated in a frequency range of the analysis target frequency as a characteristic of the diagnosis data.

10. The manufacturing apparatus of claim 1, wherein the life prediction unit includes a phase difference transition determination module using a phase difference of the vibration measured in the plurality of the accelerometers as a characteristic of the diagnosis data.

11. The manufacturing apparatus of claim 1, wherein the life prediction unit includes a Mahalanobis distance determination module setting a Mahalanobis space based on reference time series data measured by the same process conditions for measuring the diagnosis time series data at a time determined by empirical rules prior to a time to measure the diagnosis time series data and predicting a life span using a Mahalanobis distance calculated from the diagnosis data based on the Mahalanobis space.

12. A method for predicting life of a manufacturing apparatus which includes a rotary machine, comprising:

measuring diagnosis of the rotary machine in a plurality of locations where phases of vibrations are different;

generating diagnosis data based on variations in characteristics of the vibrations corresponding to an analysis target frequency by performing a frequency analysis for the diagnosis time series data; and

determining a life span of the rotary machine using the diagnosis data.

13. The method of claim 12, wherein the analysis target frequency is an integral multiple of a normal frequency unique to the rotary machine.

14. The method of claim 12, wherein the analysis target frequency is expressed as a product of an equation including a number of rotor blades of the rotary machine and a normal frequency unique to the rotary machine.

15. The method of claim 14, wherein the equation including the number of blades is expressed as [n+(1/m)], where m is the number of the blades, and n, l are arbitrary positive integers.

16. The method of claim 12, wherein the characteristics are peak accelerations of the vibrations.

17. The method of claim 12, wherein the characteristics are total accelerations integrated in a frequency range of the analysis target frequency.

18. The method of claim 12, wherein the characteristics are phase differences of the vibrations measured in the plurality of locations.

19. The method of claim 12, further comprising:

measuring reference time series data;

generating reference data based on variations in characteristics of the vibrations corresponding to the analysis target frequency by performing a frequency analysis for the reference time series data and setting a Mahalanobis space based on the reference data; and

determining the life span of the rotary machine using a Mahalanobis distance calculated from the diagnosis data based on the Mahalanobis space.

20. The method of claim 19, wherein the reference time series data are measured by the same process conditions for measuring the diagnosis time series data at a time determined by empirical rules prior to a time to measure the diagnosis time series data.