WO2023041458A3 - Computer-implemented method, modules, and system for detecting anomalies in industrial manufacturing processes - Google Patents

Computer-implemented method, modules, and system for detecting anomalies in industrial manufacturing processes Download PDF

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Publication number
WO2023041458A3
WO2023041458A3 PCT/EP2022/075214 EP2022075214W WO2023041458A3 WO 2023041458 A3 WO2023041458 A3 WO 2023041458A3 EP 2022075214 W EP2022075214 W EP 2022075214W WO 2023041458 A3 WO2023041458 A3 WO 2023041458A3
Authority
WO
WIPO (PCT)
Prior art keywords
anomalies
industrial manufacturing
computer
implemented method
manufacturing processes
Prior art date
Application number
PCT/EP2022/075214
Other languages
German (de)
French (fr)
Other versions
WO2023041458A2 (en
Inventor
Georg Schneider
Nicolas Thewes
Original Assignee
Zf Friedrichshafen Ag
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zf Friedrichshafen Ag filed Critical Zf Friedrichshafen Ag
Publication of WO2023041458A2 publication Critical patent/WO2023041458A2/en
Publication of WO2023041458A3 publication Critical patent/WO2023041458A3/en

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G05B23/0254—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks

Abstract

The invention relates to a computer-implemented method for controlling anomalies (NOK) in industrial manufacturing processes (IF), said method comprising the steps of: data-based definition of a state (Z) of a process step (PS, PS1, PS2, PS3) to be monitored of the industrial manufacturing process (IF) or of a system (A1); storing and processing data measured using a sensor and/or obtained from simulations according to the data-based state definition (A2); training an analysis model (Ref, Anno) to detect anomalies based on at least historical data (A3); integrating a verification instance (Expert) in order to control detected anomalies and generate annotations so as to extend the database (A4); detecting anomalies (A5); controlling the monitored process step (PS, PS1, PS2, PS3) or system if an anomaly is detected (A6).
PCT/EP2022/075214 2021-09-14 2022-09-12 Computer-implemented method, modules, and system for detecting anomalies in industrial manufacturing processes WO2023041458A2 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102021210107.0A DE102021210107A1 (en) 2021-09-14 2021-09-14 Computer-implemented methods, modules and systems for anomaly detection in industrial manufacturing processes
DE102021210107.0 2021-09-14

Publications (2)

Publication Number Publication Date
WO2023041458A2 WO2023041458A2 (en) 2023-03-23
WO2023041458A3 true WO2023041458A3 (en) 2023-05-11

Family

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Application Number Title Priority Date Filing Date
PCT/EP2022/075214 WO2023041458A2 (en) 2021-09-14 2022-09-12 Computer-implemented method, modules, and system for detecting anomalies in industrial manufacturing processes

Country Status (2)

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DE (1) DE102021210107A1 (en)
WO (1) WO2023041458A2 (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102022203475A1 (en) 2022-04-07 2023-10-12 Zf Friedrichshafen Ag System for generating a human-perceptible explanation output for an anomaly predicted by an anomaly detection module on high-frequency sensor data or quantities derived therefrom of an industrial manufacturing process, method and computer program for monitoring artificial intelligence-based anomaly detection in high-frequency sensor data or quantities derived therefrom of an industrial manufacturing process and method and computer program for monitoring artificial intelligence-based anomaly detection during an end-of-line acoustic test of a transmission
CN116596336B (en) * 2023-05-16 2023-10-31 合肥联宝信息技术有限公司 State evaluation method and device of electronic equipment, electronic equipment and storage medium
CN117007135B (en) * 2023-10-07 2023-12-12 东莞百舜机器人技术有限公司 Hydraulic fan automatic assembly line monitoring system based on internet of things data

Citations (6)

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DE102019112734A1 (en) * 2018-06-27 2020-01-02 Intel Corporation Improved analog functional reliability with anomaly detection
DE202019005395U1 (en) * 2019-12-20 2020-07-02 Trumpf Werkzeugmaschinen Gmbh + Co. Kg Early detection and response to errors in a machine
DE102019108268A1 (en) * 2019-03-29 2020-10-01 Festo Ag & Co. Kg Anomaly detection in a pneumatic system
DE102019110721A1 (en) * 2019-04-25 2020-10-29 Carl Zeiss Industrielle Messtechnik Gmbh WORKFLOW FOR TRAINING A CLASSIFICATOR FOR QUALITY INSPECTION IN MEASUREMENT TECHNOLOGY
US20200386656A1 (en) * 2019-06-04 2020-12-10 Palo Alto Research Center Incorporated Method and system for unsupervised anomaly detection and accountability with majority voting for high-dimensional sensor data
WO2021067385A1 (en) * 2019-09-30 2021-04-08 Amazon Technologies, Inc. Debugging and profiling of machine learning model training

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102019112734A1 (en) * 2018-06-27 2020-01-02 Intel Corporation Improved analog functional reliability with anomaly detection
DE102019108268A1 (en) * 2019-03-29 2020-10-01 Festo Ag & Co. Kg Anomaly detection in a pneumatic system
DE102019110721A1 (en) * 2019-04-25 2020-10-29 Carl Zeiss Industrielle Messtechnik Gmbh WORKFLOW FOR TRAINING A CLASSIFICATOR FOR QUALITY INSPECTION IN MEASUREMENT TECHNOLOGY
US20200386656A1 (en) * 2019-06-04 2020-12-10 Palo Alto Research Center Incorporated Method and system for unsupervised anomaly detection and accountability with majority voting for high-dimensional sensor data
WO2021067385A1 (en) * 2019-09-30 2021-04-08 Amazon Technologies, Inc. Debugging and profiling of machine learning model training
DE202019005395U1 (en) * 2019-12-20 2020-07-02 Trumpf Werkzeugmaschinen Gmbh + Co. Kg Early detection and response to errors in a machine

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Publication number Publication date
DE102021210107A1 (en) 2023-03-16
WO2023041458A2 (en) 2023-03-23

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