Datasets: Ontology-based diagnosis reporting and monitoring to improve fault finding in Industry 4.0
| dc.contributor.author | Fernández del amo blanco, Iñigo | |
| dc.contributor.author | ahmet Erkoyuncu, John | |
| dc.contributor.author | Farsi, Maryam | |
| dc.contributor.author | Bulka, Dominik | |
| dc.contributor.author | Wilding, Stephen | |
| dc.date.accessioned | 2024-06-07T03:32:36Z | |
| dc.date.available | 2024-06-07T03:32:36Z | |
| dc.date.issued | 2020-08-14 09:41 | |
| dc.description.abstract | This repository includes datasets on experimental cases of study and analysis regarding the research called "Ontology-based diagnosis reporting and monitoring to reduce no-fault-found scenarios in Industry 4.0".DOI:Abstract: "Industry 4.0 is bringing a new era of digitalisation for complex equipment. It especially benefits equipment’s monitoring and diagnostics with real-time analysis of heterogenous data sources. Management of such sources is an important research challenge. A relevant research gap involves integration of experts’ diagnosis knowledge. Experts have valuable knowledge on failure conditions that can support monitoring systems and their limitations in no-fault-found scenarios. But their knowledge is normally transferred as reports, which include unstructured data difficult to re-use. Thus, this paper proposes ontology-based diagnosis reporting and monitoring methods to capture and re-use expert knowledge for improving diagnosis efficiency. It aims to capture expert knowledge in a structured format and re-use it in monitoring systems to provide failure recommendations in no-fault-found conditions. This research conducted several methods for validating the proposed methods. Laboratory experiments present time and errors reduction rates of 20% and 12% compared to common data-driven monitoring approaches for diagnosis tasks in no-fault-found scenarios. Subject-matter experts’ surveys evidence the usability of the proposed methods to work in real-life conditions. Thus, this paper’s proposal can be considered as a method to bridge the gap for integrated data management in the context of Industry 4.0." | |
| dc.identifier.citation | Fernández del amo blanco, Iñigo; Erkoyuncu, John ahmet; Farsi, Maryam; Bulka, Dominik; Wilding, Stephen (2020). Datasets: Ontology-based diagnosis reporting and monitoring to improve fault finding in Industry 4.0. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.12279152 | |
| dc.identifier.doi | 10.17862/cranfield.rd.12279152 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/22090 | |
| dc.publisher | Cranfield University | |
| dc.rights | CC BY-NC-ND 4.0 | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | 'Data integration' | |
| dc.subject | 'Knowledge management' | |
| dc.subject | 'Fault diagnosis' | |
| dc.subject | 'Ontology-based reporting' | |
| dc.subject | 'Ontology-based monitoring' | |
| dc.subject | 'Semantic Web' | |
| dc.subject | 'Information Systems Management' | |
| dc.subject | 'Organisation of Information and Knowledge Resources' | |
| dc.subject | 'Database Management' | |
| dc.subject | 'Computer-Human Interaction' | |
| dc.title | Datasets: Ontology-based diagnosis reporting and monitoring to improve fault finding in Industry 4.0 | |
| dc.type | Dataset |