## 2 Bibliometric Analysis  
Bibliometric analysis is a method from the field of _Bibliometrics_[17]. The latter, in turn, has connotations with the term _statistical bibliography_ and constitutes a quantitative approach to literature research. In the bibliometric analysis we performed with the goal to identify existing research fields in the context of state of the art equipment maintenance systems, we follow the basic idea presented in [18], which introduced mathematical graphs, denoted as networks, as a suitable solution to represent a set of connected literature. In this network, each article is represented by a node, whereas edges constitute their bibliographic connections. In turn, connections can be established based on a multitude of similarity measures. In general, a similarity measure is based on the assumption that two articles pose a similarity in terms of thematic content. To identify entire research fields, denoted as clusters in a network (cf. Figure 2), two similarity measures are of utmost importance: _Knowledge Bases_ and _Research Fronts_[19] (cf. Figure 2). Two articles are bibliographically coupled (i.e., they are part of a research front) if they reference the same third article. Couplings between nodes become stronger (i.e., represented by a thicker line in the plotted network) if they co-cite multiple articles. By contrast, two references are connected by co-citation (i.e., they are part of a knowledge base) if they are both referenced by a third article. In addition to identify relevant research fields, we were interested in networks for authors [20], countries, and organizations, which can be established by other similarity measures. Based on the evaluation of networks, valuable results can be obtained according to [21]:  
* Identifying the research field structure
* Detecting general contextual factors
* Discovering developments in research
* Forecasting coming trends  
Another crucial aspect in performing a bibliometric analysis concerns the use of an appropriate software tool. In the field of bibliometrics, there exist tools that are specifically developed for for bibliometric analysis visualization techniques [22]). We used NetCulator [23] for the bibliometric analysis we conducted. NetCulator offers features to calculate the mentioned networks. Furthermore, it combines _bibliographic coupling_ with _lexical measures_ that, in turn, compare the similarity of terms (cf. _TF-IDF_ in Appendix A.2). Next, NetCulator calculates _hybrid second-order similarities_ (See Appendix A.2) with severe _edge cutting_[24]. The latter improves the calculation speed by ignoring all weak edges that result from weak coupling between nodes. Finally, NetCulator computes clusters based on _Louvain Clustering_[25]. Visualization and layouting for _research fronts_ and _knowledge bases_ either rely on the OpenOrd-Method [26], which provides a quick overview by automatically summarizing clusters, or the Fruchterman-Reingold algorithm [27] enabling a more detailed view. NetCulator offers many other valuable features, we utilized the context of our bibliometric analysis, e.g.,  
* Identifying core articles (i.e., significant papers within a research field).  
Figure 2: Knowledge Base, Research Front, and Sample Network.  
* Identifying frequently used terms in a research field.
* Identifying trends based on calculated timelines [28] for research fronts.  
Although the technique of bibliometric analysis is promising, it also comes with some limitations: First, the articles need to be published in an academic database. As a drawback, especially in the field of industrial production, articles are often represented as patents and internal enterprise documents. As another drawback, competition between companies leads to a large number of non-disclosure agreements. Second, any bibliometric analysis is dependent on the choice of the used search strings [29]. Finally, a time lag must be considered, as it might take years for research to be published and cited.