Version 1
: Received: 17 February 2024 / Approved: 18 February 2024 / Online: 19 February 2024 (10:09:53 CET)
How to cite:
Choudhary, A.; Vuković, M.; Mutlu, B.; Haslgrübler, M.; Kern, R. Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process. Preprints2024, 2024020943. https://doi.org/10.20944/preprints202402.0943.v1
Choudhary, A.; Vuković, M.; Mutlu, B.; Haslgrübler, M.; Kern, R. Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process. Preprints 2024, 2024020943. https://doi.org/10.20944/preprints202402.0943.v1
Choudhary, A.; Vuković, M.; Mutlu, B.; Haslgrübler, M.; Kern, R. Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process. Preprints2024, 2024020943. https://doi.org/10.20944/preprints202402.0943.v1
APA Style
Choudhary, A., Vuković, M., Mutlu, B., Haslgrübler, M., & Kern, R. (2024). Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process. Preprints. https://doi.org/10.20944/preprints202402.0943.v1
Chicago/Turabian Style
Choudhary, A., Michael Haslgrübler and Roman Kern. 2024 "Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process" Preprints. https://doi.org/10.20944/preprints202402.0943.v1
Abstract
In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoders, showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
