## 1 Motivation  
This presented literature study systematically reviews existing literature related to equipment maintenance systems to elaborate the state of the art as well as to discover current research trends. In this context, predictive maintenance (_PdM_) constitutes a specific method that aims to improve maintenance management methods, such as _run-to-failure_[1] or _preventive maintenance_[2], by including knowledge of the machine behavior with the goal to derive an optimal maintenance strategy. According to [PERSON], the common target of _predictive maintenance applications_ (_PMA_) constitutes \"[...] regular monitoring of the actual mechanical condition, operating efficiency, and other indicators of the operating condition of machine-trains [...] to ensure the maximum interval between repairs as well as to minimize the number and cost of unscheduled outages created by machine-train failures \" [3]. In general, literature reviews [4] offer a technique to structure scientific articles. Existing review types focus on different strategies and should be chosen to fit best to the problem definition. Our requirements for reviewing the field of state of the art equipment maintenance systems are (1) to cover a wide range of articles, (2) to identify contemporary maintenance procedures and techniques, (3) to discover current trends, (4) to discover highly cited and influential articles, and (5) to include latest research on equipment maintenance systems. Other literature reviews do not always satisfy these requirements, e.g., the authors of [5; 6] do not address Requirement 1. Concerning Requirement 2, for example, the authors of [7; 8] do not consider recent research. In other works, in turn, the analyzed equipment maintenance strategy is too specific [9; 10] or too general [11] compared to the literature review presented in this paper. To properly address Requirements 1-5, the literature review we accomplished uses the _bibliometric analysis_. In particular, the bibliometric analysis offers several advantages:  
* It visualizes relevant study articles based on various connection types, e.g., research fronts, knowledge bases, authors, and author affiliations. * It enables us to automatically cluster articles. * It enables quick recalculations of clusters based on different time periods. Consequently, bibliometric analyses tend to be very effective when facing large and heterogeneous data sets. Thus, we apply the bibliometric analysis in the context of our literature review on equipment maintenance systems. We believe that this research field involves a multitude of research issues as well as complex interdependencies between them. The procedure to accomplish the presented bibliometric analysis comprises ten steps as illustrated in Figure 1 in the left column. (Please note that that Figure 1 also shows in the middle and right columns, which steps have been actually accomplished for the present work.) The first step (cf. Figure 1, 1) is to select the academic databases from which relevant works shall be obtained. Frequently, the following databases are used: _Google Scholar_, _Scopus_, _Web of Science_, and _ScienceDirect_. Despite the wide-spread use of Google Scholar in Computer Science, we decided to base our review on Web of Science for two reasons. First, Web of Science and Scopus (Scopus would be another valuable source for our approach. Please note that both Web of Science and Scopus are payment platforms [12]. The reason we use just one is that our institution only has an account for Web of Science. In addition, according to [12], the differences between the two platforms constitute no crucial threat to validity.) currently generate citation data that can be comfortably used with existing bibliometric analysis software tools. Second, Web of Science is compared to Google Scholar a so-called human-curated academic database, which leads to more controlled citation data. On the other hand, Google Scholar reflects a more current status of all published papers and theses in most cases.