Artificial intelligence-based predictive maintenance, time-sensitive networking, and big data-driven algorithmic decision-making in the economics of Industrial Internet of Things

Bibliographic Data

ID19484604
AuthorsTomas Kliestik (0000-0002-3815-5409, University of Žilina), Elvira Nica (0000-0002-7383-2161, Bucharest University of Economic Studies), Pavol Durana (0000-0001-5975-1958, University of Žilina), Gheorghe H Popescu (0000-0002-6635-1861, Dimitrie Cantemir Christian University)
Year2023
Volume14
Issue4
Pages1097-1138
Publication date2023-12-30
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueOeconomia Copernicana (JOURNAL)
Journal identifiersISSN: 2083-1277 • E-ISSN: 2353-1827
PublisherInstytut Badan Gospodarczych / Institute of Economic Research (PUBLISHER)
DOI10.24136/oc.2023.033
OpenAlexW4390672874
LanguageEN
Citations received17
References cited47

Research background: The article explores the integration of Artificial Intelligence (AI) in predictive maintenance (PM) within Industrial Internet of Things (IIoT) context. It addresses the increasing importance of leveraging advanced technologies to enhance maintenance practices in industrial settings. Purpose of the article: The primary objective of the article is to investigate and demonstrate the application of AI-driven PM in the IIoT. The authors aim to shed light on the potential benefits and implications of incorporating AI into maintenance strategies within industrial environments. Methods: The article employs a research methodology focused on the practical implementation of AI algorithms for PM. It involves the analysis of data from sensors and other sources within the IIoT ecosystem to present predictive models. The methods used in the study contribute to understanding the feasibility and effectiveness of AI-driven PM solutions. Findings & value added: The article presents significant findings regarding the impact of AI-driven PM on industrial operations. It discusses how the implementation of AI technologies contributes to increased efficiency. The added value of the research lies in providing insights into the transformative potential of AI within the IIoT for optimizing maintenance practices and improving overall industrial performance

Big data · Business · Computer security · Data mining · Data science · Data-driven · Industrial Internet · Internet of Things · Predictive maintenance · Reliability engineering · Sociology · The Internet · Transformative learning · World Wide Web · Computer Science · Digital Transformation in Industry · Engineering · Machine Fault Diagnosis Techniques · Quality and Safety in Healthcare · Artificial Intelligence

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