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Open AccessArticle

Techniques and Emerging Trends for State of the Art Equipment Maintenance Systems—A Bibliometric Analysis

1
Institute of Databases and Information Systems (DBIS), Ulm University, 89081 Ulm, Germany
2
Institute of Technology and Process Management, Ulm University, 89081 Ulm, Germany
3
ATR Software GmbH, 89231, Neu-Ulm, Germany
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(6), 916; https://doi.org/10.3390/app8060916
Received: 28 March 2018 / Revised: 29 May 2018 / Accepted: 30 May 2018 / Published: 2 June 2018
The increasing interconnection of machines in industrial production on one hand, and the improved capabilities to store, retrieve, and analyze large amounts of data on the other, offer promising perspectives for maintaining production machines. Recently, predictive maintenance has gained increasing attention in the context of equipment maintenance systems. As opposed to other approaches, predictive maintenance relies on machine behavior models, which offer several advantages. In this highly interdisciplinary field, there is a lack of a literature review of relevant research fields and realization techniques. To obtain a comprehensive overview on the state of the art, large data sets of relevant literature need to be considered and, best case, be automatically partitioned into relevant research fields. A proper methodology to obtain such an overview is the bibliometric analysis method. In the presented work, we apply a bibliometric analysis to the field of equipment maintenance systems. To be more precise, we analyzed clusters of identified literature with the goal to obtain deeper insight into the related research fields. Moreover, cluster metrics reveal the importance of a single paper and an investigation of the temporal cluster development indicates the evolution of research topics. In this context, we introduce a new measure to compare results from different time periods in an appropriate way. In turn, among others, this simplifies the analysis of topics, with a vast amount of subtopics. Altogether, the obtained results particularly provide a comprehensive overview of established techniques and emerging trends for equipment maintenance systems. View Full-Text
Keywords: bibliometric analysis; predictive maintenance; research front graph; article density; trend analysis bibliometric analysis; predictive maintenance; research front graph; article density; trend analysis
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Hoppenstedt, B.; Pryss, R.; Stelzer, B.; Meyer-Brötz, F.; Kammerer, K.; Treß, A.; Reichert, M. Techniques and Emerging Trends for State of the Art Equipment Maintenance Systems—A Bibliometric Analysis. Appl. Sci. 2018, 8, 916.

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