Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning
- In discrete manufacturing, the knowledge about causal relationships makes it possible to avoid unforeseen production downtimes by identifying their root causes. Learning causal structures from real-world settings remains challenging due to high-dimensional data, a mix of discrete and continuous variables, and requirements for preprocessing log data under the causal perspective. In our work, we address these challenges proposing a process for causal reasoning based on raw machine log data from production monitoring. Within this process, we define a set of transformation rules to extract independent and identically distributed observations. Further, we incorporate a variable selection step to handle high-dimensionality and a discretization step to include continuous variables. We enrich a commonly used causal structure learning algorithm with domain-related orientation rules, which provides a basis for causal reasoning. We demonstrate the process on a real-world dataset from a globally operating precision mechanical engineering company.In discrete manufacturing, the knowledge about causal relationships makes it possible to avoid unforeseen production downtimes by identifying their root causes. Learning causal structures from real-world settings remains challenging due to high-dimensional data, a mix of discrete and continuous variables, and requirements for preprocessing log data under the causal perspective. In our work, we address these challenges proposing a process for causal reasoning based on raw machine log data from production monitoring. Within this process, we define a set of transformation rules to extract independent and identically distributed observations. Further, we incorporate a variable selection step to handle high-dimensionality and a discretization step to include continuous variables. We enrich a commonly used causal structure learning algorithm with domain-related orientation rules, which provides a basis for causal reasoning. We demonstrate the process on a real-world dataset from a globally operating precision mechanical engineering company. The dataset contains over 40 million log data entries from production monitoring of a single machine. In this context, we determine the causal structures embedded in operational processes. Further, we examine causal effects to support machine operators in avoiding unforeseen production stops, i.e., by detaining machine operators from drawing false conclusions on impacting factors of unforeseen production stops based on experience.…
Author details: | Christopher HagedornORCiDGND, Johannes HuegleORCiD, Rainer SchlosserORCiDGND |
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DOI: | https://doi.org/10.1007/s10845-022-01952-x |
ISSN: | 0956-5515 |
ISSN: | 1572-8145 |
Title of parent work (English): | Journal of intelligent manufacturing |
Publisher: | Springer |
Place of publishing: | Dordrecht |
Publication type: | Article |
Language: | English |
Date of first publication: | 2022/05/23 |
Publication year: | 2022 |
Release date: | 2023/11/27 |
Tag: | Causal inference; Causal structure learning; Log data; Manufacturing; industry |
Volume: | 33 |
Issue: | 7 |
Number of pages: | 17 |
First page: | 2027 |
Last Page: | 2043 |
Funding institution: | Projekt DEAL |
Organizational units: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
Peer review: | Referiert |
Publishing method: | Open Access / Hybrid Open-Access |
License (German): | CC-BY - Namensnennung 4.0 International |