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Design knowledge for deep-learning-enabled image-based decision support systems — evidence from power line maintenance decision-making

Landwehr, Julius; Kühl, Niklas ORCID iD icon; Walk, Jannis; Gnädig, Mario

Abstract:

With the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The maturing research on vision-based power line inspection driven by advancements in deep learning offers first possibilities to move towards more holistic, automated, and safe decision-making.

However, (current) research focuses solely on the extraction of information rather than its implementation in decision-making processes. This paper addresses this shortcoming by designing, instantiating, and evaluating a holistic deep-learning-enabled image-based decision support system artifact for power line maintenance at a German distribution system operator in southern Germany.

Following the design science research paradigm two main components of the artifact are designed: A deep-learning-based model component responsible for automatic fault detection of power line parts as well as a user-oriented interface responsible for presenting the captured information in a way that enables more informed decisions. ... mehr

Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 1867-0202, 2363-7005
KITopen-ID: 1000141247
Erschienen in Business & information systems engineering
Verlag Springer
Band 64
Heft 6
Seiten 707–728
Nachgewiesen in Scopus
Web of Science
Dimensions

Verlagsausgabe §
DOI: 10.5445/IR/1000141247
Veröffentlicht am 01.07.2022
Originalveröffentlichung
DOI: 10.1007/s12599-022-00745-z
Scopus
Zitationen: 8
Web of Science
Zitationen: 5
Dimensions
Zitationen: 11
Seitenaufrufe: 196
seit 17.12.2021
Downloads: 143
seit 04.07.2022
Cover der Publikation
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