## 5 Importance Measure  
By collecting all lead documents for each time period, we obtain lists with potentially relevant papers. Please note that the importance of literature entries in these lists is not directly comparable for two reasons. First of all, when judging the importance of a paper, not only the number of its citations should be considered. Additionally, for two papers with same number of citations, their date of publication should be considered as well. For this purpose, we introduce a correction factor \(tc_{n}\) (cf. Equation (2)). Second, the _citing culture_ differs across the various research fields. For example, in economics, the mean number of citations is higher than in computer science. In line with the latter observation, regarding the results of search string _Lean Maintenance_, we obtain articles with high numbers of citations that mainly focus on management-oriented issues ([46; 50]). To reduce these two effects and to express aspects from the bibliometric analysis more properly, we propose an alternative impact measure. (For a general overview of impact measures see [86]). The first part of the importance measure represents anomalies caused by the different search strings (Predictive Maintenance (PDM), Condition Monitoring (CM), Predictive Analytics (PA), Reliability-centered Maintenance (RCM), Lean Maintenance (LM), and Smart Maintenance (SM)). Assuming that the search strings differ in their relevance for state of the art equipment maintenance systems, we express their importance on scale from 0 to 1. This factor is denoted as _topic neighborhood_ (tn) assigned to each article \(a\):  
\[tn(a)= \begin{cases}1.0,a\in\{PDM\}\\ 0.7,a\in\{CM\}\\ 0.4,a\in\{PA\cup RCM\cup LM\cup SM\}\end{cases}  \tag{1}\]  
As opposed to the _h-index_[36], which cannot decrease over time, we apply a logarithmic reduction (cf. Figure 16) for the value of a citation. For example, Figure 16 shows a decreasing, normalized influence value for 88 fictive citations over a time span of 18 years. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**\#** & **Year** & **Lead Document** & **Citations** & **Cluster Label** & **Technique** \\ \hline
1 & 2005 & [75] & 591 & Motor Analysis (2) & Motor Current Signature Analysis \\
2 & 2006 & [76] & 936 & Prognostics and Health Management (3) & Sensor Data Fusion \\
3 & 2007 & [77] & 284 & Motor analysis (2) & Support Vector Machine \\
4 & 2008 & [78] & 187 & Motor analysis (2) & Principle Component Analysis \\
5 & 2009 & [79] & 297 & Wind Turbines (3) & Fault Detection System \\
6 & 2010 & [80] & 375 & Sensor-based Systems (2) & Wearable Systems \\
7 & 2011 & [72] & 248 & Monitoring (2) & Remaining Useful Life \\
8 & 2012 & [81] & 380 & Data-driven Systems & Process Monitoring \\
9 & 2013 & [82] & 90 & Thermography (2),(3) & IR-Camera \\
10 & 2014 & [83] & 243 & Monitoring (2) & Data-Driven Approach \\
11 & 2015 & [84] & 243 & Monitoring (2) & Data-Based Techniques \\
12 & 2016 & [85] & 1 & Cyber-physical Systems & Adaptive Learning \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Research field and technique of the most cited papers or corresponding clusters 2005–2012. Since the bibliometric analysis is based on clustering, it seems to be appropriate to include the relative _cluster size_ (cs) of a cluster \(c\) in period \(p\) as well. In addition, the average _h-index_ of the first and last author will be part of the importance measure to express that literature from authors widely read is very important. Finally, graph-based indicators contribute to the importance measure. The important papers from a cluster can be identified using the _article degree_, also named as well valency [87]. Besides, according to the graph theory, a cluster can be denoted as a graph \(G\), with each article representing a vertex \(v\), where _deg(v)_ is the number of edges incident to the vertex. A second graph unit is _betweenness_[88], which constitutes a _centrality measure_. Betweenness represents the number of shortest paths in the cluster that cross \(v\).