## 4 Bibliometric Analysis Results
### Predictive Maintenance
#### 4.1.1 Period 1990-2000  
First of all, the results for the first period are based on 175 articles. The first work for this period was initiated by the American Association for Artificial Intelligence, whose subgroup, called _Special Interest in Manufacturing_ (SIGMAN), organized workshops collecting ideas in this context. Since an American organization was pioneering the topic of predictive maintenance in this period, the leading publication nation was the US (29.7%), followed by France (8.0%) and Canada (7.4%). According to Figure 9, we can observe an increasing cooperation of countries over time and a stronger thematic focus. More specifically, each data point in Figure 9 represents a country, a close distance between them indicates researching in similar topics. However, closeness does not necessarily display collaborations, but constitutes an indicator of a high knowledge transfer between the involved countries. In general, the research in this first period did not reveal any outstanding research topic. Accordingly, no leading author or institution could be identified. The keyword analysis for the first period (cf. Table 2) shows a strong connection between the terms _predictive maintenance_ and _monitoring_ as they belong to the same cluster (i.e., Cluster \(1\) from Table 2. Furthermore, as monitoring is represented by the two keywords _condition monitoring_ and _monitoring_, this topic is a leading one in this period. Figure 8: Visualizations in each Period. Figure 7: Calculation Parameters (Improved Readability). Interestingly, _monitoring_ and _predictive maintenance_ are in the same cluster, while this does not apply to condition monitoring, i.e., in this period, despite the thematic closeness of _condition monitoring_ and _predictive maintenance_, they were addressed independently. Please note that that this result supports our decision to consider _condition monitoring_ as a separate search string. In general, we assume the following order of complexity for tasks in the context of equipment maintenance systems:  
_Report \(\to\) Analysis \(\to\) Monitoring \(\to\) Prediction_  
Furthermore, we assume the technique _monitoring_ needs mainly humans as decision makers and rarely relies on techniques of _machine learning_. After calculating research fronts, we sort the articles based on the number of citations. The most popular paper, according to citations, is denoted as _lead document_. In a manual inspection, used techniques of the lead document and a thematic field for each cluster have been identified. The collected information are shown in Table 3, where the thematic field is denoted as _cluster label_. The manual labeling of each cluster, denoted as cluster caption, and the identification of the used technique considers top terms, scanning the resulting articles. Please note that not every article in each research front needs to deal with the topic the cluster caption implies. In general, a bigger cluster size increases the probability of a _false-positive_ cluster classification. The research front clusters from Table 3 are explained in the following. Cluster 1 deals with the continuous status monitoring. This cluster is coined by a joint project between the US and Egypt, as represented by the Alexandria National Iron & Steel Company. Second, in Cluster 2, items were found dealing with _vibration analysis_. Here, vibrations caused by for example pumps, spindles and motors are used to refer to abnormal machine behavior. Cluster 3 assembles literature that deals with maintenance in risky environments, such as oil refinery and nuclear power plants. With a focus on special electronic equipment, such as radars and motors, Cluster 4 contains research based on the question how to predict electric systems behavior.