## 6 Forecast  
We forecast the development of identified research fronts based on the history of scientific and public interest. According to [90], _scanning_ constitutes a prerequisite of forecasting. Scanning involves \"collecting information: the system, history, and context of the issue [...]\". We collected the required information from our bibliometric analysis. For this forecast, we only include those research fields connected to search string _predictive maintenance_. Thereby, we assume that a higher interest in the topic will result in more publications. Furthermore, we distinguish between _scientific_ and _public interest_. To describe the _scientific interest_, six research fronts from the period 2008-2016 were chosen and their names were used as new search queries for Web of Science. This period constitutes the latest one and, hence, the most important period for us. From this period, we take six important research fronts and analyze their development over the last 20 years; i.e., according to the published items per year.. This time, we were not interested in extracting literature, but in measuring the published items per year (cf. Figure 17). Interestingly, the development over the last 20 years has shown a significant growth in almost all connected research fields. In particular, the research front _Internet of Things_ is identified to be a potential driving force for publications in the context of predictive maintenance. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \# & **Cite** & **Times Cited** & **Cluster Size** & **H-Index** & **Degree** & **Betweenness** & **Importance** \\ \hline
1 & [59] & 0.03 & 0.43 & 0.40 & 1 & 1 & 0.59 \\
2 & [89] & 0.28 & 0.05 & 0.43 & 0.28 & 1 & 0.43 \\
3 & [76] & 1.00 & 0.18 & 0.36 & 0.18 & 0.02 & 0.40 \\
4 & [38] & 0.07 & 0.32 & 0.33 & 0.18 & 0.90 & 0.36 \\
5 & [75] & 0.62 & 0.43 & 0.26 & 0.32 & 0.02 & 0.35 \\ \hline
31 & [47] & 0.89 & 0.15 & 0.02 & 0.31 & 0.06 & 0.14 \\
32 & [51] & 0.05 & 0.43 & 0.38 & 0.15 & 0.00 & 0.14 \\ \hline
73 & [69] & 0.01 & 0.03 & 0.31 & 0.14 & 0.00 & 0.06 \\
74 & [67] & 0.02 & 0.03 & 0.02 & 0.14 & 0.00 & 0.05 \\ \hline \multicolumn{2}{c}{_avg_} & 0.1 & 0.21 & 0.25 & 0.21 & 0.10 & 0.15 \\ \multicolumn{2}{c}{_med_} & 0.05 & 0.18 & 0.19 & 0.17 & 0.03 & 0.12 \\ \multicolumn{2}{c}{_std_} & 0.16 & 0.16 & 0.21 & 0.12 & 0.22 & 0.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Article Ranking Sorted by Importance MeasureAs an indicator for _public interest_, we choose the Google trend analysis. ([[https://www.google.de/trends/explore](https://www.google.de/trends/explore)]([https://www.google.de/trends/explore](https://www.google.de/trends/explore))) Interest, as measured by Google searches, is visualized in a normalized form for the period from 2004 to 2017 (cf. Figure 18). The topic _Internet of Things_ grew significantly during the last years. The greatest difference between scientific and public interest can be observed in the graphs related to _software maintenance_. Please note that the Fukushima Daiichi nuclear disaster in March 2001 distorts the graph for _nuclear power plants_. The public interest was enormous due to this event, causing a peak in the graph. Furthermore, we link upcoming developments in the field of predictive maintenance to the development of the _Internet of Things_. Three aspects are of particular interest in this context:  
* _Algorithm Development_. Current success stories on _deep learning_ have not affected research yet. In particular, neural networks were used in all periods. Please note that the introduction of  
Figure 17: Published Items Per Year. Figure 18: Google Trend Visualization. _convolutional neural networks_ (cnn) might improve predictions. So far, this technique has been widely used for image classification [91], but the tendency of bringing more and more sensors into industrial applications will make this multidimensional approach more attractive as well. * _Trust._ Using predictive systems, the importance of _planning algorithms_ for industrial production is increasing.