Experimentation on NN Models for Hazard Identification in Machinery Functional Safety
| 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: | AI; Experimental evaluation; Functional Safety; hazard identification; Hazards; machinery functional safety; Model configuration; model configurations; Neural Network; Neural network model; Neural-networks; Open-source; Rasa; Systematic study | Erscheinungsdatum: | 2023 | Herausgeber: | Institute of Electrical and Electronics Engineers Inc. | Journal: | IEEE International Conference on Industrial Informatics (INDIN) | Volumen: | 2023-July | Zusammenfassung: | The use of Artificial Intelligence (AI) in machinery functional safety can enhance efficiency and accuracy by automating tasks previously carried out by humans. This paper presents an experimental evaluation of Neural Network (NN) models for hazard identification in machinery functional safety. The systematic study includes own implementations of NN models using open source building blocks and the use of an open source conversational AI framework with various pipeline configurations. The paper provides a comparative analysis of the qualitative and quantitative parameters for the models and configurations. © 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/INDIN51400.2023.10218319 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171158300&doi=10.1109%2fINDIN51400.2023.10218319&partnerID=40&md5=c8ab04eb15d6b4ba86759d6c999e52b8 |
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