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

• US 7,065,469 B2
• Filed: 03/19/2003
• Issued: 06/20/2006
• Est. Priority Date: 09/27/2002
• Status: Expired due to Fees

• 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
• Primary Examiner(s): Hoff, Marc S.
• Assistant Examiner(s): West, Jeffrey R
• Application Number:
  US10/390,698
• Publication Number:
  US 20040064277A1
• Time in Patent Office: 1,189 Days
• Field of Search: 702182-185, 702 33- 36, 702 56- 59, 702/66, 702 70- 72, 702 75- 77, 73/11.4, 73/18.4, 73/542.8, 736/60, 736/72
• US Class Current: 702/183
• 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 of vibrations of the rotary machine, attached to the rotary machine at locations where variations in the vibrations 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 from the analyzed diagnosis time series data based on variations in characteristics of the vibrations corresponding to an analysis target frequency and to record the diagnosis data, the analysis target frequency being expressed as a product of an equation including a reciprocal number of rotor blades of the rotary machine and a normal frequency unique to the rotary machine, the equation including the reciprocal number of the rotor blades expressed as n+(l/m), where m is the number of the blades, and n, l are arbitrary positive integers; 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 the variations in the vibrations 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 an end of the rotary machine.

5. 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.

6. The manufacturing apparatus of claim 1, wherein the equation including the reciprocal number of blades is expressed as [1+(1/m)].

7. 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 under process conditions which are 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.

8. The manufacturing apparatus of claim 1, wherein the characteristics of the vibrations are peak accelerations of the vibrations.

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

implementing a manufacturing process under process conditions in the manufacturing apparatus;

measuring diagnosis time series data of vibrations of the rotary machine in a plurality of locations where variations in the vibrations are different;

generating first 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, the analysis target frequency being expressed as a product of an equation including a reciprocal number of rotor blades of the rotary machine and a normal frequency unique to the rotary machine, the equation including the reciprocal number of blades expressed as n+(l/m), where m is the number of the rotor blades, and n, l are arbitrary positive integers; and

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

10. The method of claim 9, wherein the equation including the reciprocal number of blades is expressed as [1+(1/m)].

11. The method of claim 9, wherein the characteristics of the vibrations are peak accelerations of the vibrations.

12. The method of claim 9, wherein the plurality of locations are a first surface of the rotary machine and a second surface of the rotary machine facing to the first surface.

13. The method of claim 9, wherein the plurality of locations are locations where phases of the variations in the vibrations of the rotary machine are different.

14. The method of claim 13, wherein the plurality of locations are near a center of the rotary machine and an end of the rotary machine.

15. The method of claim 9, further comprising:

measuring reference time series data including a plurality of time series data characteristics;

generating reference data based on variations in the time series data characteristics, 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 first diagnosis data based on the Mahalanobis space.

16. The method of claim 15, wherein the reference time series data are measured for the same process conditions as the diagnosis time series data, prior to a time to measure the diagnosis time series data.