A Chatbot Assistant for Reducing Risk in Machinery Design

Autor(en): Iyenghar, Padma
Kieviet, Michael
Pulvermueller, Elke 
Wuebbelmann, Juergen
Herausgeber: Dorksen, H.
Scanzio, S.
Jasperneite, J.
Wisniewski, L.
Man, K.F.
Sauter, T.
Seno, L.
Trsek, H.
Vyatkin, V.
Stichwörter: Artificial intelligence; Artificial Intelligence (AI); chatbot; Chatbots; Functional Safety; hazard identification; Hazards; Machine design; Machinery design; machinery functional safety; Model configuration; model configurations; Neural network model; Neural network models; Risk assessment; risk reduction; Risks reduction; Safety awareness
Erscheinungsdatum: 2023
Herausgeber: Institute of Electrical and Electronics Engineers Inc.
Journal: IEEE International Conference on Industrial Informatics (INDIN)
Volumen: 2023-July
Zusammenfassung: 
In this paper, a novel chatbot for risk reduction as an aid during machinery design is presented. The general workflow of the chatbot involves the identification of the hazard described by the user using a neural network model followed by an interactive dialog based conversation, in which the risk reduction measures are outlined. A prototype implementation of the chatbot presents the steps to generate and pre-process the training data for Artificial Intelligence (AI) based models. Different neural network models are trained and evaluated for the proposed risk reduction chatbot. A comparative study is presented by employing an in-depth qualitative and quantitative evaluation. The work presented in this paper shows significant promise in ensuring safety awareness, thereby aiding in implementing functional safety in the early stages of machinery design and development. © 2023 IEEE.
Beschreibung: 
Cited by: 0; Conference name: 21st IEEE International Conference on Industrial Informatics, INDIN 2023; Conference date: 17 July 2023 through 20 July 2023; Conference code: 192026
ISBN: 9781665493130
ISSN: 1935-4576
DOI: 10.1109/INDIN51400.2023.10218134
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171134385&doi=10.1109%2fINDIN51400.2023.10218134&partnerID=40&md5=b422f1421b300174dff341a56e189d4d

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