DATA COLLECTING METHOD FOR DETECTION AND ON-TIME WARNING SYSTEM OF INDUSTRIAL PROCESS

• US 20120271826A1
• Filed: 04/18/2011
• Published: 10/25/2012
• Est. Priority Date: 04/19/2010
• Status: Abandoned Application

• Current Assignee: BNF Technology Inc.
• Original Assignee: BNF Technology Inc.
• Inventors: Kim, Su Young
• Application Number:
  US13/519,070
• Publication Number:
  US 20120271826A1
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G05B 23/021 adopting a different treatment of each operating region or a different mode of the monitored system, e.g. transient modes; different operating configurations of monitored system
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

A data collection method for a process margin monitoring system of industrial equipment includes preparing a learning data set based on data determined to be normal in an operation history of the industrial equipment so that the learning data set is sorted for each operation mode, in a case in which the industrial equipment includes equipment units performing the same functions, receiving data for each of the equipment units and processing the received data as data for the equipment units, sorting and grouping associated ones of the data in the learning data set, and sampling the collected data to reduce the amount of data.

Claims

1. A data collection method for a process margin monitoring system of industrial equipment, comprising:

preparing a learning data set based on data determined to be normal in an operation history of the industrial equipment so that the learning data set is sorted for each operation mode;

in a case in which the industrial equipment comprises a plurality of equipment units performing the same functions, receiving data for each of the equipment units and processing the received data as data for the equipment units;

sorting and grouping associated ones of the data in the learning data set; and

sampling the collected data to reduce the number of data.

2. The data collection method according to claim 1, wherein the learning data set comprises a first data set to an N-th data set (N being a natural number equal to or greater than 2) depending upon a scale of data to be collected or time when data are collected.

3. The data collection method according to claim 2, wherein the first data set comprises signals related to a specific equipment unit of the industrial equipment for monitoring process margin of the specific equipment unit, the second data set comprises signals related to the entirety of the industrial equipment for monitoring process margin of the entirety of the industrial equipment, and

the third data set comprises signals regarding the entirety or a portion of the industrial equipment immediately after a specific event is generated in the entirety or the portion of the industrial equipment.

4. The data collection method according to claim 1, further comprising, in a case in which the learning data set comprises data displayed as digital signals, collecting analog signal that can substitute for the digital signal and converting the digital signal into the analog signal.

5. The data collection method according to claim 1, wherein the grouping step comprises:

regarding variables, a correlation coefficient between which is equal to or greater to a set value, as belonging to the same group;

calculating a smoothness parameter with respect to the variables regarded as belonging to the same group using a 4-fold validation method;

putting combinations of all variables in the group besides the variables regarded as belonging to the same group to calculate a square sum of residuals while calculating the smoothness parameter using the 4-fold validation method; and

in a case in which a decrease ratio of a square sum of residuals immediately after a square sum of specific residuals to the square sum of specific residuals is equal to or less than a set value, terminating grouping at a time when the square sum of specific residuals is calculated.

6. The data collection method according to claim 5, wherein the step of calculating the square sum of residuals comprises sorting and using only variables related to characteristics of the equipment among the variables besides the variables regarded as belonging to the same group in consideration of characteristics of the equipment.

7. The data collection method according to claim 5, wherein the correlation coefficient is analyzed by the following mathematical expression.

8. The data collection method according to claim 1, wherein the data sampling step comprises performing dispersion of a value of a specific variable on the basis of a grid size to reduce the number of data related to the variable in a corresponding grid.

9. The data collection method according to claim 1, wherein the data sampling step comprises calculating standard deviation (σ

_X) of a value of a specific variable and reducing the number of data related to the variable in a corresponding grid on the basis of a grid size (GridSize_X) calculated by the following mathematical expression according to set resolution.

10. The data collection method according to claim 8, wherein the number of data left in the grid is decided by the product of the number of data related to the variable in the corresponding grid and a set rate, and at least one of the data is left in each grid.

11. A storage medium for storing a data collection method according to claim 1, wherein the data collection method is computer programmed.

12. The data collection method according to claim 9, wherein the number of data left in the grid is decided by the product of the number of data related to the variable in the corresponding grid and a set rate, and at least one of the data is left in each grid.

13. A storage medium for storing a data collection method according to claim 2, wherein the data collection method is computer programmed.

14. A storage medium for storing a data collection method according to claim 3, wherein the data collection method is computer programmed.

15. A storage medium for storing a data collection method according to claim 4, wherein the data collection method is computer programmed.

16. A storage medium for storing a data collection method according to claim 5, wherein the data collection method is computer programmed.

17. A storage medium for storing a data collection method according to claim 6, wherein the data collection method is computer programmed.

18. A storage medium for storing a data collection method according to claim 7, wherein the data collection method is computer programmed.

19. A storage medium for storing a data collection method according to claim 8, wherein the data collection method is computer programmed.

20. A storage medium for storing a data collection method according to claim 9, wherein the data collection method is computer programmed.