Data driven method for automated detection of anomalous work pieces during a production process

US11468274B2 · kind B2 · utility

Assignee

• Siemens Aktiengesellschaft · DE

Inventors

• Denis Krompaß · Munich, DE
• Hans-Georg Köpken · Erlangen, DE

Key dates

• Filing date: Dec 6, 2018
• Grant date: Oct 11, 2022
• Expiry date: Jul 17, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G05B2219/32193
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Provided is a method and system for detection of anomalous work pieces that includes computing at least one deviation data signal for a target data signal of a target work piece with respect to reference data signals recorded for a corresponding production process step of a set of reference work pieces, performing a stepwise anomaly detection by data processing of the at least one computed deviation data signal and a process type indicator indicating a type of the production process step using a trained anomaly detection data model to calculate for each time step or path length step of the production process step an anomaly probability that the respective time step or path length step is anomalous, and classifying the target work piece and/or the production process step as being anomalous or not anomalous on the basis of the calculated anomaly probabilities.