Artificial intelligence-based predictive maintenance, time-sensitive networking, and big data-driven algorithmic decision-making in the economics of Industrial Internet of Things
Tomáš Klieštik, Elvira Nica, Pavol Ďurana, Gheorghe H. Popescu
Abstract
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.