ADJUSTMENT OF DATA COLLECTION RATE BASED ON ANOMALY DETECTION

• US 20090089558A1
• Filed: 09/27/2007
• Published: 04/02/2009
• Est. Priority Date: 09/27/2007
• Status: Active Grant

• Current Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Original Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Inventors: Balewski, Mark Joseph, Harris, Kendal R., Gach, Martin George, Hall, Kenwood H., Rischar, Charles Martin, Bradford, Jonathan D., Kretschmann, Robert J., Siorek, Timothy
• Granted Patent: US 7,657,333 B2
• Time in Patent Office: Days
• US Class Current: 712/225
• CPC Class Codes: G05B 21/02 electric
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

Systems and methods that vary multiple data sampling rates, to collect sets of data with different levels of granularity for an industrial system. The data for such industrial system includes sets of data from the “internal” data stream(s) (e.g., history data collected from an industrial unit) and sets of data from an “external” (e.g., traffic data on network services) data stream(s), based in part on the criticality/importance criteria assigned to each collection stage. Each set of data can be assigned its own unique data collection rate. For example, a higher sample rate can be employed when collecting data from the network during an operation stage that is deemed more critical (e.g., dynamic attribution of predetermined importance factors) than the rest of the operation.

Claims

1. An industrial automation system, comprising:

a coordination component that synchronizes multiple data streams associated with the industrial automation system; and

a sample adjustment component that varies a data sampling rate of the multiple data streams.

2. The industrial automation system of claim 1, the multiple data streams include a set of multiple internal data streams or multiple external data streams or a combination thereof.

3. The industrial automation system of claim 1 further comprising a recognition component that identifies trends in the multiple data streams.

4. The industrial automation system of claim 1 further comprising a centralized data collection that stores a unified repository of data from the multiple data streams.

5. The industrial automation system of claim 1, the sample rate adjustment component further comprising an estimation component that automatically predicts a required data sampling rate for a stage of an operation associated with the industrial automation system.

6. The industrial automation system of claim 1, further comprising a network with an embedded network traffic analyzer (NTA).

7. The industrial automation system of claim 1 further comprising a matching component that subscribes modules or industrial zones with predetermined triggering events or phases of an industrial process.

8. The industrial automation system of claim 5, the NTA comprising a control component that facilitates controls of a subset of the network based in part upon an analysis of network data by the NTA.

9. The industrial automation system of claim 1 further comprising an artificial intelligence component that facilitates rate adjustment for data collection.

10. The industrial automation system of claim 2 further comprising a statistical model constructed based on prior collected data.

11. The industrial automation system of claim 10 further comprising a feedback to adjust the statistical model.

12. The industrial automation system of claim 11 further comprising triggering events defined as part of initiation for adjustment of sampling rates.

13. A method of collecting data within an industrial plant comprising:

identifying a process for collection of multiple data streams;

maintaining a sequence relationship between the multiple internal data and the external data streams; and

adjusting rate of data collection for the process.

14. The method of claim 13 further comprising determining functional blocks associated with the process.

15. The method of claim 14 further comprising defining a plurality of triggering events that correspond to execution of a granularity level associated with the functional blocks.

16. The method of claim 15 further comprising providing feedback regarding efficiency of collecting data at the granularity level.

17. The method of claim 15 further comprising collecting data based on the triggering event.

18. The method of claim 15 further comprising collecting historian data across various levels of the industrial plant.

19. The method of claim 15 further comprising embedding a network traffic analyzer as part of a network.

20. An industrial system comprising:

collecting means for collecting multiple data streams related to an industrial process; and

means for adjusting rate of data collection for the collecting means.