Method, system and device for predictive error recognition in a plant

• US 20050066241A1
• Filed: 12/01/2003
• Published: 03/24/2005
• Est. Priority Date: 09/24/2003
• Status: Active Grant

• Current Assignee: Siemens AG
• Original Assignee: Siemens AG
• Inventors: Schuderer, Christian, Gross, Wolfgang
• Granted Patent: US 7,162,312 B2
• Time in Patent Office: Days
• US Class Current: 714/48
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• 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

Method, system and device for predictive recognition of errors in a manufacturing system, said comprising: online monitoring of data of the manufacturing system; and online comparing said data with archived error patterns to predict errors supposed to occur in the manufacturing system; whereby the archived error patterns are built and recognized by statistical methods and data mining methods. The present invention can be used for discrete or continuous or batch processes.

Claims

1. A method for predictive recognition of errors in a manufacturing system, said method comprising the steps of:

archiving a plurality of error patterns that previously occurred in the manufacturing system, the archived error patterns created by statistical methods;

monitoring manufacturing system data in real-time; and

comparing the monitored data with the archived error patterns in real-time to predict imminent errors likely to occur in the manufacturing system.

2. A method according claim 1, wherein the archived error pattern containing compressed information.

3. A method according claim 2, wherein the compressed information for the archived error patterns is achieved by statistical methods or data mining mechanisms.

4. A method according claim 1, wherein the comparing of the data is performed by data mining mechanisms.

5. The method according to claim 1, wherein the archived error patterns are automatically built by the monitored data using statistical methods or data mining mechanisms.

6. The method according to claim 1, wherein the monitored data are pre-specified data.

7. The method according to claim 6, wherein the pre-specified data are minimized using structural information of the manufacturing system.

8. A method according claim 1, further comprising storing the monitored data in a ring-puffer.

9. A method according claim 1, wherein the monitored data are automatically read out components of the manufacturing system.

10. A method according to claim 1, further comprising triggering corrective actions.

11. A method according to claim 1, wherein the method is adapted for discrete or continuous or batch processes.

12. A computerized system for predictive recognition of errors in a manufacturing system, comprising:

a mechanism for archiving a plurality of error patterns that previously occurred in the manufacturing system, the archived error patterns created by statistical methods;

a mechanism for monitoring manufacturing system data in real-time; and

a mechanism for comparing the monitored data with the archived error patterns in real-time to predict imminent errors likely to occur in the manufacturing system.

13. A system according claim 12, wherein the comparing of the data is performed by data mining mechanisms.

14. A system according to claim 12, wherein the archived error patterns are automatically built by the monitored data using statistical methods or data mining mechanisms.

15. A system according to claim 12, wherein the monitored data are pre-specified data.

16. A device for predictive recognition of errors in a manufacturing system, comprising:

a mechanism for archiving a plurality of error patterns that previously occurred in the manufacturing system, the archived error patterns created by statistical methods;

a mechanism for monitoring manufacturing system data in real-time; and

a mechanism for comparing the monitored data with the archived error patterns in real-time to predict imminent errors likely to occur in the manufacturing system.

17. A device according to claim 16, wherein the device is a dedicated unit in a manufacturing environment.

18. A device according to claim 16, wherein the device is a decentral net component.

19. A device according to claim 16, wherein the device is a field device.

20. A device according to claim 16, wherein the device is a PLC.