Correlation of non-times series events in industrial systems

• US 7,908,360 B2
• Filed: 09/28/2007
• Issued: 03/15/2011
• Est. Priority Date: 09/28/2007
• 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, Bradford, Jonathan D., Siorek, Timothy, Hall, Kenwood H., Rischar, Charles Martin, Kretschmann, Robert J.
• Primary Examiner(s): WON, MICHAEL YOUNG
• Application Number:
  US11/863,510
• Publication Number:
  US 20090089233A1
• Time in Patent Office: 1,264 Days
• Field of Search: 709/224, 709/231, 709/232, 709/248
• US Class Current: 709/224
• CPC Class Codes: G05B 19/4185 characterised by the network communication
• CPC Class Codes: G05B 2219/31217 Merge, synchronize process data and network data for trend analysis
• CPC Class Codes: H04L 41/0631 using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
• CPC Class Codes: H04L 41/22 comprising specially adapted graphical user interfaces [GUI]
• CPC Class Codes: H04L 43/106 using time related information in packets, e.g. by adding timestamps
• CPC Class Codes: H04L 43/18 Protocol analysers
• 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 correlate among disparate pieces of synchronized data, collected from an “internal” data stream (e.g., history data collected from an industrial unit) and an “external” data stream (e.g., traffic data on network services). A process trend component that determines/predicts an outcome of an industrial process and facilitates diagnostics/prognostics of an industrial system. Accordingly, relations among various parameters can be discovered (e.g., dynamically) and proper corrective adjustments supplied to the industrial process. Such enables a tight control and short reaction time to process parameters, and for a modification thereof.

Claims

1. An industrial automation system, comprising:

a coordination component that synchronizes data from multiple data streams from a plurality of sources associated with the industrial automation system, wherein each of the multiple data streams contains data that is distinct from data of others of the multiple data streams, wherein synchronizing the data includes maintaining at least one of a timing or sequence relationship between events represented by the data; and

a process trend component that predicts an outcome of operations for the industrial automation system based on analysis of the synchronized data.

2. The industrial automation system of claim 1, the multiple data streams include at least one internal data stream and at least one external data stream, wherein the at least one internal data stream comprises data from one or more industrial control components and the at least one external data stream comprises data from one or more networks.

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, wherein the data further comprising messages that caused the events.

5. The industrial automation system of claim 1 further comprising heuristic models that predict outcome for a stage of a manufacturing 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 6, 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 correlation among disparate pieces of data.

10. The industrial automation system of claim 9 further comprising a statistical model constructed based on prior collected data.

11. The industrial automation system of claim 1, wherein the coordination component further weaves the data from the multiple data streams together into an interrelated data presentation based upon the synchronization.

12. The industrial automation system of claim 1, wherein the coordination component further adjusts a data sampling rate associated with at least of the multiple data streams based upon an importance criteria assigned to a data collection stage associated with the at least one of the multiple data streams.

13. A method of adapting processes in an industrial process within an industrial plant comprising:

employing a processor executing computer executable instructions embodied on a computer readable storage medium to perform the following acts;

identifying a process for collection of multiple data streams, wherein each of the multiple data streams contains data that is distinct from data of others of the multiple data streams;

maintaining at least one of a timing or sequence relationship between events represented by the data; and

modifying the industrial process in real time based on the analysis of the synchronized data, wherein the analysis comprises predicting an outcome of the industrial process.

14. The method of claim 13, wherein the multiple data streams include at least one internal data stream and at least one external data stream, wherein the at least one internal data stream comprises data from one or more industrial controllers and the at least one external data stream comprises data from one or more networks.

15. The method of claim 14 further comprising weaving the data from the multiple data streams together into an interrelated data presentation based upon the at least of the timing or sequence relationship.

16. The method of claim 15 further comprising adjusting a data sampling rate associated with at least of the multiple data streams based upon an importance criteria assigned to a data collection stage associated with the at least one of the multiple data streams.

17. An industrial system comprising:

means for collecting multiple data streams related to an industrial process, wherein each of the multiple data streams contains data that is distinct from data of others of the multiple data streams;

means for correlating data collected by the means for collecting, wherein correlating the data includes synchronizing at least one of a timing or sequence relationship between events represented by the data; and

means for predicting an outcome of operations for the industrial automation system based on analysis of the synchronized data.

18. The industrial controller system of claim 17, wherein the multiple data streams include at least one internal data stream and at least one external data stream, wherein the at least one internal data stream comprises data from one or more industrial controllers and the at least one external data stream comprises data from one or more networks.

19. The industrial controller of claim 18, wherein the means for correlating weaves the data from the multiple data streams together into an interrelated data presentation based upon the at least of the timing or sequence relationship.