## 4 Bibliometric Analysis Results
### Predictive Maintenance
#### 4.1.3 Period 2008-2016  
Again, first of all, research fronts are calculated and evaluated (cf. Table 10). The newly emerging clusters are presented in the following. First, one of the added clusters deals with _multi-component applications_ (1), that aim to combine data of multiple production units into one predictive model. Figure 10: Research Front Graph. With increasing interest in alternative energies, _wind turbines_ (4) come into the focus of predictive maintenance systems. Third, the discipline known as _prognostics and health management_ (5), which links failure mechanisms to system life cycle management, forms a cluster as well. Furthermore cluster _Cloud and IoT_ (6) aims at better production results by, first and foremost, including recent developments from _Big Data Analytics_ and _Distributed Systems_. Dealing with certain machine components, the analyzes of _rolling bearings_ (7) and _capacitors_ (8) form other clusters. Literature referring to the prediction of metal deterioration due to _corrosion_ is collected in (11), whereas _rail maintenance_ (14) monitors the current state of roads and rails. Finally, by analyzing changes in _oil_ (15) consistency and consumption, a changing machine behavior must be considered. Concerning the publishing countries, China increases its number of publications, while the US remain number one. In turn, the Tsinghua University (Peking) constitutes now the driving force for publications in this topic. After discussing the results of the periods separately, keywords, research fronts, and techniques over all three periods are juxtaposed in the following. A first possibility of showing the development of research during the time periods is the comparison of top keywords. Table 4 shows the top mentioned key words, their percentage distribution, and their difference in ranking position compared to the previous period (cf. Section 4.1.1). The ranking difference is denoted as \(\Delta\), whereby a positive \(\Delta\) denotes growing importance of this keyword. Furthermore, letter \(n\) stands for _not mentioned yet_, meaning that this keyword was not introduced in a previous period. Two particular developments can be obtained from Table 4. First, the complexity of predictive maintenance systems rises over time. During the first period, there is a focus on rather simple methods, such as monitoring and preventive maintenance, while their importance decrease during the last period, in which more complex issues (e.g., prediction and prognostics) are addressed. An example of this development can be seen in the ratio between _fault detection_ and _fault diagnosis_. During the first period, they are equally important, while in the last period the diagnostic part is more important. As a second aspect, the used techniques are not explicitly mentioned anymore in the last period. The disappearance of techniques like _neural networks_ and _genetic algorithms_, which had an outstanding ranking position before, indicates that they are now in a rather major state, i.e., they are no longer explicitly mentioned. Figure 11: Published Literature. Research Front Development  
To gain insights into the development of research fronts, we sort all identified research fronts according to their relative size in the period and search for recurrences (cf. Figure 12). Furthermore, we identify highly overlapping subjects, using the graphical representation of research fronts, and put related research fronts in brackets. As vibration analysis is constantly present in all periods, it is being revealed as the most important research field. In addition, we discovered that the thematic field _Internet of Things_ gains importance over time and it is connected to generic approaches as well. Technique Identification  
A final comparison over all three time periods aims to identify the techniques applied in the maintenance approaches. Thereby, for the lead document of each cluster, the used technique is extracted from the abstract and keywords as well as by evaluating the context of the document.