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SemML: Facilitating development of ML models for condition monitoring with semantics

Zhou, Baifan; Svetashova, Yulia; Gusmao, Andre; Soylu, Ahmet; Cheng, Gong; Mikut, Ralf ORCID iD icon; Waaler, Arild; Kharlamov, Evgeny

Abstract (englisch):

Monitoring of the state, performance, quality of operations and other parameters of equipment and production processes, which is typically referred to as condition monitoring, is an important common practice in many industries including manufacturing, oil and gas, chemical and process industry. In the age of Industry 4.0, where the aim is a deep degree of production automation, unprecedented amounts of data are generated by equipment and processes, and this enables adoption of Machine Learning (ML) approaches for condition monitoring. Development of such ML models is challenging. On the one hand, it requires collaborative work of experts from different areas, including data scientists, engineers, process experts, and managers with asymmetric backgrounds. On the other hand, there is high variety and diversity of data relevant for condition monitoring. Both factors hampers ML modelling for condition monitoring. In this work, we address these challenges by empowering ML-based condition monitoring with semantic technologies. To this end we propose a software system SemML that allows to reuse and generalise ML pipelines for conditions monitoring by relying on semantics. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000139197
Veröffentlicht am 17.01.2022
DOI: 10.1016/j.websem.2021.100664
Zitationen: 20
Web of Science
Zitationen: 12
Zitationen: 18
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 11.2021
Sprache Englisch
Identifikator ISSN: 1570-8268
KITopen-ID: 1000139197
HGF-Programm 47.14.02 (POF IV, LK 01) Information Storage and Processing in the Cell Nucleus
Erschienen in Web semantics
Verlag Elsevier
Band 71
Seiten Art.Nr. 100664
Vorab online veröffentlicht am 01.10.2021
Schlagwörter Condition monitoring, Ontologies, Templates, Data integration, Machine learning, Software architecture, Industry 4.0
Nachgewiesen in Dimensions
Web of Science
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