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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.

Details

Title
Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories
Author
Kaupp, Lukas 1   VIAFID ORCID Logo  ; Nazemi, Kawa 2   VIAFID ORCID Logo  ; Humm, Bernhard 3   VIAFID ORCID Logo 

 Faculty of Computer Science, Darmstadt University of Applied Sciences, Haardtring 100, 64295 Darmstadt, Germany 
 Research Group Human-Computer Interaction and Visual Analytics, Faculty of Media, Darmstadt University of Applied Sciences, Haardtring 100, 64295 Darmstadt, Germany 
 Faculty of Computer Science, Darmstadt University of Applied Sciences, Haardtring 100, 64295 Darmstadt, Germany; Hessian Center for Artificial Intelligence, Mornewegstraße 30, 64293 Darmstadt, Germany 
First page
3942
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20799292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2748519186
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.