System and method for analyzing data

• US 20040172409A1
• Filed: 02/28/2003
• Published: 09/02/2004
• Est. Priority Date: 02/28/2003
• Status: Active Grant

• Current Assignee: Eaton Intelligent Power Limited (Eaton Corporation PLC.)
• Original Assignee: Eaton Corp. (Wisconsin) (Eaton Corporation PLC.)
• Inventors: James, Frederick Earl
• Granted Patent: US 7,487,148 B2
• Time in Patent Office: Days
• US Class Current: 707/104.1
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures

Abstract

A system and method for analyzing data (the “system”) is disclosed. The system can automatically identify patterns of template data points encapsulated in the form of one or more “events.” Calculations and analysis relating to those identified events can be automatically performed at the identified locations of the events. Events are user-defined, and can be defined in reference to multiple channels of data. The system can perform various correlation calculations in comparing events with data points. Upon identifying the location of various events in the various data files, markers can be placed at those file locations. Analysis calculations can then be performed related to the marked data. The system can incorporate the automated time-scaling of patterns, marker sorting heuristics, the adjustment of fit sensitivity based on the size of the pattern, target value weighing, and the calculation of various confidence values relating to the processing of the system.

Claims

1. A system for analyzing data, comprising:

a data subsystem, including a file, said file comprising a plurality of data points;

an interface subsystem, including a plurality of input characteristics and a plurality of events, wherein said interface subsystem creates said events from at least one said input characteristic; and

an analysis subsystem, including a plurality of markers, wherein said analysis subsystem searches said data subsystem for said data points indicative of said events, and wherein said analysis subsystem places said markers on said data points indicative of said events without human intervention.

2. The system of claim 1, further comprising a plurality of template data types and a plurality of template data points, wherein at least one said event includes said plurality of template data points and said plurality of template data types.

3. The system of claim 1, further comprising a plurality of data types, wherein said file includes said plurality of data types.

4. The system of claim 1, further comprising:

a plurality of data types, a plurality of template data types, and a plurality of template data points;

wherein at least one said event includes said plurality of template data points;

wherein at least one said event includes said plurality of data types; and

wherein said file includes said plurality of data types.

5. The system of claim 4, wherein each data type in said plurality of template data types is not represented in said plurality of data types.

6. The system of claim 4, wherein each template data type in said plurality of data types is not represented in said plurality of template data types.

7. The system of claim 1, further comprising a weight factor, wherein said analysis subsystem uses said weight factor to identify at least one said event.

8. The system of claim 1, said analysis subsystem further including a marker location associated with said marker and an analysis, wherein said analysis subsystem generates said analysis from at least one said data point at said marker location.

9. The system of claim 8, wherein said analysis is generated without human intervention.

10. The system of claim 1, said data subsystem further including a plurality of files in a pre-defined and user-defined format, wherein said plurality of data points are stored within said plurality of files.

11. The system of claim 1, said data subsystem further including a data collection module, said data collection module including a sensor for capturing sensor data, wherein said data collection module generates said data points from said sensor data.

12. The system of claim 11, wherein said sensor collects sensor data for a plurality of channels in a substantially simultaneous manner, and wherein each channel is of a different data type.

13. The system of claim 12, wherein one channel in said plurality of channels is a force channel.

14. The system of claim 1, wherein said markers are placed at a plurality of marker locations, wherein said marker locations are identified by a correlation-based matching heuristic, and wherein a confidence value is associated with the identification of each said marker locations.

15. The system of claim 1, wherein said markers are placed at a plurality of marker locations, wherein said marker locations are identified by a correlation-based matching heuristic, and wherein a confidence value is associated with the identification of said marker locations.

16. The system of claim 1, said analysis subsystem including a skill level indicator and a menu comprising of a plurality of menu selections, wherein said skill level indicator is set in accordance with at least one said input characteristic, and wherein said menu selections are selectively disabled depending on said skill level indicator.

17. The system of claim 1, wherein at least one said event is stored in a pattern array, wherein a subset of said plurality of data points are stored in a data array, and wherein a correlation heuristic is applied to said pattern array and said data array to identify a marker location.

18. The system of claim 1, further comprising a confidence value and a threshold value, wherein a confidence value is calculated by said analysis subsystem, and wherein said confidence value is compared to said threshold value.

19. The system of claim 1, further comprising a pattern matching heuristic, wherein said analysis subsystem identifies a marker location with said pattern matching heuristic.

20. The system of claim 1, further comprising a sample size adjustment, wherein said analysis subsystem identifies said marker using said sample size adjustment.

21. The system of claim 1, further comprising a scaling adjustment, wherein said analysis subsystem identifies said marker using said scaling adjustment.

22. A system for analyzing data comprising:

a data subsystem, including a plurality of data types and plurality of files comprising a plurality of data points, wherein each said data point is associated with at least one said data type;

a pattern subsystem, including a plurality of events, a plurality of input characteristics, and a plurality of template data points, wherein said events are defined from said input plurality of characteristics, and wherein at least two said template data points are associated with each said event; and

a search subsystem, including a plurality of markers, wherein said search subsystem identifies a plurality of locations indicative of said events within said data subsystem, and wherein said search subsystem places said markers at said locations.

23. The system of claim 22, further comprising an analysis module including an analysis calculation, wherein said analysis calculation is generated from said data points at said location of said marker.

24. The system of claim 22, said search subsystem further including a search criteria and a search result, wherein said plurality of files are searched for said search criteria, and wherein said search result includes at least two files.

25. The system of claim 24, wherein said search criteria includes a plurality of data types.

26. The system of claim 22, further comprising a reporting tool, a configurable report, and a plurality of locations, wherein said search subsystem automatically generates a plurality of markers at said plurality of locations, and wherein said reporting tool automatically generates said configurable report from said data points at said plurality of locations.

27. The system of claim 22, further comprising a plurality of confidence values and a plurality of locations, wherein at least one said confidence value is generated at each said location.

28. The system of claim 22, further comprising a confidence value, a threshold value, and an analysis module, wherein an analysis module generates said confidence value from at least one marker, and wherein said analysis module compares said confidence value to said threshold value.

29. The system of claim 22, further comprising a fit sensitivity, wherein said fit sensitivity is automatically adjusted for the number of said template data points associated with said event.

30. The system of claim 22, further comprising an analysis calculation, and a marker sort heuristic, wherein said calculation is performed with said marker sort heuristic.

31. The system of claim 22, further comprising a target value weighing heuristic, wherein said system performs said target value weighing heuristic to identify said event.

32. The system of claim 22, further comprising a marker control, wherein said marker control provides for moving said location.

33. A method for locating an event in a data file, comprising:

defining a plurality of events in terms of a plurality of template data points;

accessing a file of data points; and

identifying the location of said events in said file without human intervention by comparing said template data points with said data points.

34. The method of claim 33, further comprising:

loading said data points into a data array;

storing said template data points into a pattern array;

calculating a correlation value between said data array and said pattern array; and

placing one or more markers at a file location of the data points.

35. The method of claim 33, further comprising generating a confidence value to represent the degree of confidence in the identification of at least one location of at least one event.

36. The method of claim 35, further comprising comparing said confidence value to a threshold value.

37. The method of claim 33, further comprising performing a user-defined analysis heuristic at said marker location without human intervention.

38. The method of claim 33, further comprising adjusting the time scaling of the template data points stored in said pattern array without human intervention.

39. The method of claim 33, further comprising adjusting a fit sensitivity value used in calculating a correlation value, wherein said fit sensitivity value is based on the number of data points associated with the event.

40. The method of claim 33, further comprising loading an excluded array to exclude template data points and data points from the comparison of template data points and data points.

41. The method of claim 33, further comprising:

selecting an operating mode from one of automatic, batch, and manual; and

invoking predetermined processing rules in accordance with the selected operating mode.

42. The method of claim 33, further comprising selecting two or more channels from a plurality of channels.

43. The method of claim 33, further comprising setting an always rematch flag to yes.

44. The method of claim 33, further comprising invoking a pattern recognition heuristic.

45. The method of claim 33, further comprising setting a user skill level.

46. The method of claim 33, wherein a number of channels for determining each marker placement is set by the user to a value greater than 3.

47. The method of claim 33, further comprising automatically recalculating a calculation at the marker location using updated pattern information.

48. The method of claim 33, further comprising performing a target value heuristic.

49. The method of claim 33, further comprising sorting a plurality files based on an overall confidence value.

50. The method of claim 33, further comprising identifying a user expertise level.

51. The method of claim 33, further comprising calculating a weight factor, wherein said weight factor is used to identify the plurality of locations.

52. The method of claim 33, further comprising performing a pattern size adjustment heuristic.

53. The method of claim 33, further comprising sorting said plurality of markers.