Maintenance concepts evolution: a comparative review towards Advanced Maintenance conceptualization

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Recently, there has been a growing interest in issues related to maintenance performance management, which is confirmed by a significant number of publications and reports devoted to these problems. However, theoretical and application studies indicate a lack of research on the systematic literature reviews and surveys of studies that would focus on the evolution of Industry 4.0 technologies used in the maintenance area in a cross-sectional manner. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology includes bibliometric performance analysis and a review of the systematic literature. First, the general bibliometric analysis was conducted based on the literature in Scopus and Web of Science databases. Later, the systematic search was performed using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012-2022), studies published in English, and studies found in the selected databases. In addition, the authors focused on research work within the scope of the Maintenance 4.0 study. Therefore, papers within the following research fields were selected: (a) augmented reality, (b) virtual reality, (c) system architecture, (d) data-driven decision, (e) Operator 4.0, and (f) cybersecurity. This resulted in the selection of the 214 most relevant papers in the investigated area. Finally, the selected articles in this review were categorized into five groups: (1) Data-driven decision-making in Maintenance 4.0, (2) Operator 4.0, (3) Virtual and Augmented reality in maintenance, (4) Maintenance system architecture, and (5) Cybersecurity in maintenance. The obtained results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives.

A Semantic Model in the Context of Maintenance: A Predictive Maintenance Case Study

Advanced technologies in modern industry collect massive volumes of data from a plethora of sources, such as processes, machines, components, and documents. This also applies to predictive maintenance. To provide access to these data in a standard and structured way, researchers and practitioners need to design and develop a semantic model of maintenance entities to build a reference ontology for maintenance. To date, there have been numerous studies combining the domain of predictive maintenance and ontology engineering. However, such earlier works, which focused on semantic interoperability to exchange data with standardized meanings, did not fully leverage the opportunities provided by data federation to elaborate these semantic technologies further. Therefore, in this paper, we fill this research gap by addressing interoperability in smart manufacturing and the issue of federating different data formats effectively by using semantic technologies in the context of maintenance. Furthermore, we introduce a semantic model in the form of an ontology for mapping relevant data. The proposed solution is validated and verified using an industrial implementation.