System and method for determining the effectiveness of production installations, fault events and the causes of faults

• US 20030014699A1
• Filed: 07/29/2002
• Published: 01/16/2003
• Est. Priority Date: 01/29/2000
• Status: Active Grant

• Current Assignee: Intellectual Ventures II LLC (Intellectual Ventures LLC)
• Original Assignee: ABB Research Ltd. (ABB Ltd.)
• Inventors: Greulich, Manuel, Milanovic, Raiko, Vollmar, Gerhard, Kallela, Jari
• Granted Patent: US 6,760,639 B2
• Time in Patent Office: Days
• US Class Current: 714/47
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G05B 2219/32179 Quality control, monitor production tool with multiple sensors
• CPC Class Codes: G05B 2219/32214 Display on screen what fault and which tool and what order to repair fault
• CPC Class Codes: G05B 2219/32221 Correlation between defect and measured parameters to find origin of defect
• CPC Class Codes: G05B 23/0272 Presentation of monitored results, e.g. selection of status reports to be displayed; Filtering information to the user
• CPC Class Codes: G05B 23/0278 Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
• CPC Class Codes: Y10S 706/912 Manufacturing or machine, e.g. agricultural machinery, machine tool

Abstract

A method and a system are described for determining the effectiveness of production installations, significant fault events that bring about deviations from a desired effectiveness and the causes of fault events. The production installation is connected to a data acquisition device, which is set up for continuous acquisition and ready-to-call-up storage of installation and production-related data. A service device is connected to the data acquisition device and has an input device for the input of additional installation and production-related descriptive data that cannot be called up from the data acquisition device. An online system part is set up for calling up installation and production-related data from the data acquisition device, calculating the effectiveness, detecting fault events, determining significant fault events by fault event evaluation, and determining in each case the causes of faults. An offline system part is provided and contains a number of generic fault models and assessment models.

Claims

1. A system for determining an effectiveness of production installations of various types, significant fault events which bring about deviations from a prescribed desired effectiveness, and causes of the significant fault events, the system comprising:

a data acquisition device to be connected to a respective production installation and set up for continuous acquisition and ready-to-call-up storage of data including installation-related data and production-related data; and

a service device connected to said data acquisition device, said service device including;

an input device for inputting additional descriptive data including installation-related descriptive data and production-related descriptive data that cannot be called up from said data acquisition device;

a display device for displaying the effectiveness determined, the significant fault events and the causes of the significant fault events;

an online system part connected to and set up for calling up the installation-related data and the production-related data from said data acquisition device, said online system part having a fault event detector detecting the significant fault events on a basis of the data, the additional descriptive data input by said input device, and on an overall equipment effectiveness (OEE) script, said online system part determining the significant fault events by fault event evaluation using a configured assessment model, determining in each case the causes of the significant fault events using a configured fault model, and calculating the effectiveness;

an offline system part connected to said online system part, said offline system part one of containing and has access to a number of models including generic fault models and assessment models, said offline system part set up for searching for the models on a basis of at least one of called-up and input descriptive data, said offline system part configuring the models and storing the models one of locally and in a locally distributed form, said offline system part configured for storing the models in said online system part as one of the configured assessment model and the configured fault model.

2. The system according to claim 1, wherein said respective production installation is selected from the group consisting of a single machine and an installation having a number of machines.

3. The system according to claim 1, wherein said data acquisition device is part of one of a master control system and a programmable controller.

4. The system according to claim 1, wherein said service device is set up for using a web browser to access models which are a stored on a web server and for storing configured models there.

5. The system according to claim 1, wherein said online system part has an OEE calculator set up for calculating the effectiveness by using a stored OEE calculation formula.

6. The system according to claim 1, wherein said fault event detector is set up for detecting the significant fault events by limit value monitoring the OEE script.

7. The system according to claim 1, wherein said online system part is set up for determining the significant fault events using a Pareto analysis and said configured assessment model.

8. The system according to claim 1, wherein said online system part includes a cause determiner set up for determining causes of the significant event faults by using fault event data and one of the configured fault model and an expert system.

9. The system according to claim 1, wherein said service device is set up for at least one of determining recommendations for eliminating the significant faults events, visually presenting the significant fault events and outputting the significant fault events for further transmission.

10. The system according to claim 1, wherein said offline system part has a model searcher and a library storing the generic fault models for finding a best model, the best model being a fault model of which a fault event description is most similar to a respective search inquiry.

11. The system according to claim 1, wherein said offline system part includes a model configurer and a model editor connected to said model configurer for configuring the generic fault models.

12. The system according to claim 10, wherein said offline system part includes a model generalizer for generalizing configured models and for storing the configured models in said library for reuse.

13. The system according to claim 10, wherein said offline system part includes a model editor and with the aid of said model editor a search inquiry can be formulated for said model searcher.

15. The method according to claim 14, which comprises carrying out the continuous calculation of the effectiveness using the OEE calculator and by accessing the fault events stored in the fault database.

16. The method according to claim 14, which comprises editing additional fault events, which cannot be detected by the fault event detector using a configured OEE calculation script, using a fault event input.

17. The method according to claim 14, which comprises:

determining significant fault events using a fault event evaluator; and

presenting visually the significant fault events in a Pareto diagram.

18. The method according to claim 14, which comprises:

using a model searcher for searching by use of descriptive data stored in a model library for that generic model which best matches a specific fault event and the production installation; and

feeding the generic model to a model editor and to a model configurer for forming configured models, the configured models are used for an evaluation of fault events and for a cause analysis.

19. The method according to claim 18, wherein for determining the causes of faults by the cause determiner, cause hypotheses of a configured error model which contains cause-effect diagrams extending over a number of model levels are verified by the cause determiner using the descriptive data, the configured error model being worked step by step from one level to the next until an actual cause is found.

20. The method according to claim 14, which comprises using a fault model, which has a recommendation model added to it and with the aid of which recommendations for eliminating faults are determined and output.

21. The method according to claim 18, which comprises:

generalizing models configured in a course of the method by a model generalizer for later reuse resulting in generalized models; and

storing the generalized models in a model library, elements of a respective model being one of generalized and removed.

14. A method for automatically determining an effectiveness of a production installation, significant fault events and causes of the effectiveness deviating from a prescribed desired state, which comprises the steps of:

calling up productivity-relevant historical data acquired and stored by a data acquisition device connected to the production installation using a fault event detector;

inputting additional data including installation-related data and production-related data;

carrying out a continuous calculation of the effectiveness using an OEE calculator and a suitable method;

performing an investigation of the data with regard to fault event patterns using a fault event detector;

storing detected fault events as time series in a fault database;

identifying the significant fault events from the detected fault events using a fault event evaluation and a stored configured assessment model;

determining causes of faults using a cause determiner with respect to a respective significant fault event, taking into account additionally input data containing a description of specific environmental conditions; and

presenting the causes of faults determined and the effectiveness determined at least one of visually and as a data output.