STATE MONITORING SYSTEM, STATE MONITORING METHOD AND MEDIUM

• US 20150276557A1
• Filed: 03/27/2015
• Published: 10/01/2015
• Est. Priority Date: 03/28/2014
• Status: Abandoned Application

• Current Assignee: Hitachi High-Technologies Corporation (Hitachi, Ltd.)
• Original Assignee: Hitachi High-Technologies Corporation (Hitachi, Ltd.)
• Inventors: MASUDA, Toshio, KOGA, Mutsuki, NISHIKAWA, Hideo, KOKUMA, Junichi, TADA, Nobuyoshi
• Application Number:
  US14/671,042
• Publication Number:
  US 20150276557A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01M 99/008 by doing functionality tests
• CPC Class Codes: G05B 23/0243 model based detection method, e.g. first-principles knowledge model

Abstract

A state monitoring system enabling a sign of abnormality of equipment to be detected is disclosed, which includes: a storage unit to be stored with normal models obtained by analyzing, per series of manipulations, time-series learning data of sensor outputs indicated by respective units of processing equipment when normally finishing processing a raw material through the series of manipulations according to a default sequence; and a processing unit to diagnose a state of the processing equipment on the occasion of processing a specified raw material, upon an input of time-series evaluation data of the sensor output indicated by each of the units of the processing equipment on the occasion of finishing processing the specified raw material through the series of manipulations, on the basis of a comparison between the inputted data and the normal model.

Claims

1. A state monitoring system comprising:

a storage unit to be stored with normal models obtained by analyzing, per series of manipulations, time-series learning data of sensor outputs indicated by respective units of processing equipment when normally finishing processing a raw material through the series of manipulations according to a default sequence; and

a processing unit to diagnose a state of the processing equipment on the occasion of processing a specified raw material, upon an input of time-series evaluation data of the sensor output indicated by each of the units of the processing equipment on the occasion of finishing processing the specified raw material through the series of manipulations, on the basis of a comparison between the inputted evaluation data and the normal model.

2. The state monitoring system according to claim 1, wherein the storage unit is stored with, per type of product, the normal model obtained by analyzing the learning data per type of the product generated by processing the raw material, and

the processing unit diagnoses, per type of the product, the state of the processing equipment, upon the input of the evaluation data, on the basis of the comparison between the inputted evaluation data and the normal model.

3. The state monitoring system according to claim 1, wherein the storage unit is stored with the normal model obtained by analyzing, per system, the time-series learning data of the sensor output indicated by each of the units of the processing equipment including a plurality of systems to process the raw material in parallel, and

the processing unit diagnoses, per system, the state of the processing equipment, upon the input of the evaluation data, on the basis of the comparison between the inputted evaluation data and the normal model.

4. The state monitoring system according to claim 2, wherein the storage unit is stored with the normal model obtained by analyzing, per system, the time-series learning data of the sensor output indicated by each of the units of the processing equipment including a plurality of systems to process the raw material in parallel, and

the processing unit diagnoses, per system, the state of the processing equipment, upon the input of the evaluation data, on the basis of the comparison between the inputted evaluation data and the normal model.

5. The state monitoring system according to claim 1, wherein the storage unit is stored with the normal model obtained by analyzing the time-series learning data of the sensor output indicated by each of the units of a specified system of the processing equipment including a plurality of systems to process the raw material in parallel, and

the processing unit diagnoses the state of the processing equipment, upon the input of the evaluation data of each system, on the basis of a comparison between the inputted evaluation data of each system and the normal model obtained from the learning data of the specified system.

6. The state monitoring system according to claim 2, wherein the storage unit is stored with the normal model obtained by analyzing the time-series learning data of the sensor output indicated by each of the units of a specified system of the processing equipment including a plurality of systems to process the raw material in parallel, and

the processing unit diagnoses the state of the processing equipment, upon the input of the evaluation data of each system, on the basis of a comparison between the inputted evaluation data of each system and the normal model obtained from the learning data of the specified system.

7. The state monitoring system according to claim 1, wherein the processing unit diagnoses, based on an anomaly measure, the state of the processing equipment when processing the specified raw material by calculating the anomaly measure, upon the input of the evaluation data, on the basis of a comparison between the inputted evaluation data and the normal model.

8. The state monitoring system according to claim 2, wherein the processing unit diagnoses, based on an anomaly measure, the state of the processing equipment when processing the specified raw material by calculating the anomaly measure, upon the input of the evaluation data, on the basis of a comparison between the inputted evaluation data and the normal model.

9. The state monitoring system according to claim 3, wherein the processing unit diagnoses, based on an anomaly measure, the state of the processing equipment when processing the specified raw material by calculating the anomaly measure, upon the input of the evaluation data, on the basis of a comparison between the inputted evaluation data and the normal model.

10. The state monitoring system according to claim 4, wherein the processing unit diagnoses, based on an anomaly measure, the state of the processing equipment when processing the specified raw material by calculating the anomaly measure, upon the input of the evaluation data, on the basis of a comparison between the inputted evaluation data and the normal model.

11. A state monitoring method comprising:

storing normal models obtained by analyzing, per series of manipulations, time-series learning data of sensor outputs indicated by respective units of processing equipment when normally finishing processing a raw material through the series of manipulations according to a default sequence in a storage unit; and

diagnosing a state of the processing equipment on the occasion of processing a specified raw material, upon an input of time-series evaluation data of the sensor output indicated by each of the units of the processing equipment on the occasion of finishing processing the specified raw material through the series of manipulations, on the basis of a comparison between the inputted evaluation data and the normal model.

12. The state monitoring method according to claim 11, further comprising;

storing, on the occasion of storing the normal model in the storage unit, per type of product, the normal model obtained by analyzing the learning data per type of the product generated by processing the raw material; and

diagnosing, per type of the product, the state of the processing equipment, on the occasion of diagnosing the state of the processing equipment, on the basis of the comparison between the inputted evaluation data and the normal model.

13. The state monitoring method according to claim 11, further comprising:

storing, on the occasion of storing the normal model in the storage unit, the normal model obtained by analyzing, per system, the time-series learning data of the sensor output indicated by each of the units of the processing equipment including a plurality of systems to process the raw material in parallel; and

diagnosing, per system, the state of the processing equipment, on the occasion of diagnosing the state of the processing equipment, on the basis of the comparison between the inputted evaluation data and the normal model.

14. The state monitoring method according to claim 11, further comprising:

storing, on the occasion of storing the normal model in the storage unit, the normal model obtained by analyzing the time-series learning data of the sensor output indicated by each of the units of a specified system of the processing equipment including a plurality of systems to process the raw material in parallel; and

diagnosing, on the occasion of diagnosing the state of the processing equipment, the state of the processing equipment, on the basis of a comparison between the inputted evaluation data of each system and the normal model obtained from the learning data of the specified system.

15. The state monitoring method according to claim 11, further comprising:

diagnosing, on the occasion of diagnosing the state of the processing equipment, the state of the processing equipment based on an anomaly measure when processing the specified raw material by calculating the anomaly measure on the basis of a comparison between the inputted evaluation data and the normal model.

16. A non-transitory computer-readable recording medium having stored therein a program for causing a computer to execute a state monitoring process comprising:

storing normal models obtained by analyzing, per series of manipulations, time-series learning data of sensor outputs indicated by respective units of processing equipment when normally finishing processing a raw material through the series of manipulations according to a default sequence in a storage unit; and

diagnosing a state of the processing equipment on the occasion of processing a specified raw material, upon an input of time-series evaluation data of the sensor output indicated by each of the units of the processing equipment on the occasion of finishing processing the specified raw material through the series of manipulations, on the basis of a comparison between the inputted evaluation data and the normal model.

17. The non-transitory computer-readable recording medium having stored therein a program for causing a computer to execute a state monitoring process according to claim 16, further comprising;

storing, on the occasion of storing the normal model in the storage unit, per type of product, the normal model obtained by analyzing the learning data per type of the product generated by processing the raw material; and

diagnosing, per type of the product, the state of the processing equipment, on the occasion of diagnosing the state of the processing equipment, on the basis of the comparison between the inputted evaluation data and the normal model.

18. The non-transitory computer-readable recording medium having stored therein a program for causing a computer to execute a state monitoring process according to claim 16, further comprising;

storing, on the occasion of storing the normal model in the storage unit, the normal model obtained by analyzing, per system, the time-series learning data of the sensor output indicated by each of the units of the processing equipment including a plurality of systems to process the raw material in parallel; and

diagnosing, per system, the state of the processing equipment, on the occasion of diagnosing the state of the processing equipment, on the basis of the comparison between the inputted evaluation data and the normal model.

19. The non-transitory computer-readable recording medium having stored therein a program for causing a computer to execute a state monitoring process according to claim 16, further comprising;

storing, on the occasion of storing the normal model in the storage unit, the normal model obtained by analyzing the time-series learning data of the sensor output indicated by each of the units of a specified system of the processing equipment including a plurality of systems to process the raw material in parallel; and

diagnosing, on the occasion of diagnosing the state of the processing equipment, the state of the processing equipment, on the basis of a comparison between the inputted evaluation data of each system and the normal model obtained from the learning data of the specified system.

20. The non-transitory computer-readable recording medium having stored therein a program for causing a computer to execute a state monitoring process according to claim 16, further comprising;

diagnosing, on the occasion of diagnosing the state of the processing equipment, the state of the processing equipment based on an anomaly measure when processing the specified raw material by calculating the anomaly measure on the basis of a comparison between the inputted evaluation data and the normal model.