Root Cause Analysis for Manufacturing using Semantic Web Technologies
Strobel T (2023)
Publication Type: Conference contribution, Conference Contribution
Publication year: 2023
Book Volume: Proceedings of the Doctoral Consortium at ISWC 2023
Event location: Athens
URI: https://ceur-ws.org/Vol-3678/paper13.pdf
Open Access Link: https://ceur-ws.org/Vol-3678/paper13.pdf
Abstract
Root Cause Analysis is a method to identify the cause of problems in manufacturing. The analysis is usually performed manually by experts, is time-consuming, and costly. Therefore, the automation of the analysis process is of interest. However, besides measurements from manufacturing processes, prior knowledge is needed to create results comparable to those from manual analysis. This work uses Semantic Web technologies to model prior knowledge and Neuro-Symbolic AI to reason for root causes. The Neuro-Symbolic AI combines reasoning on observed production data and on prior knowledge. Using such approach makes it possible to combine efficient pattern recognition and probabilistic reasoning for data analysis. This work shows an evaluation workflow for such proposed methods with a data generation model. Preliminary results show the conceptualization of available prior manufacturing knowledge. The future steps contain the research on data models and the evaluation of existing Neuro-Symbolic approaches.
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How to cite
APA:
Strobel, T. (2023). Root Cause Analysis for Manufacturing using Semantic Web Technologies. In Proceedings of the International Semantic Web Conference. Athens, GR.
MLA:
Strobel, Tim. "Root Cause Analysis for Manufacturing using Semantic Web Technologies." Proceedings of the International Semantic Web Conference, Athens 2023.
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