## 2 Methodology
The details of the keywords show the most important concepts are the following (by decreasing occurrence):  
* Fault detection;
* Fault diagnosis;
* Rolling bearing;* Signal processing;
* Empirical mode decomposition;
* Vibration analysis;
* Support vector machines;
* Singular value decomposition;
* Neural networks;
* Pattern recognition;
* K-means clustering;
* Gears;
* Principal component analysis;
* Reliability;
* Batch process;
* Penicillin fermentation;
* Feature extraction;
* Wavelet transforms;
* Reliability;
* Fermentation;
* Discriminant analysis. Figure 1: Literature research methodology. Moreover, the graphical analysis evidences the connections between the keywords. The \"rolling bearings\" are closely linked to the concept of \"vibration\", \"feature extraction\" and the \"k-means algorithm\". Another connection is available between the concept of \"batch processes\" and \"fermentation processes\", which is also related to the technique of \"principal component analysis\" and the concept of \"discriminant analysis\". Similarly, the \"signal processing\" forms a cluster with \"singular value decomposition\" and \"empirical mode decomposition\". Finally, as one might expect, the themes of fault diagnosis and fault detection are extremely cross-cutting. This is not surprising, as they are two of the main objectives of condition monitoring. the concept of feature extraction, although important, is less transversal and more circumscribed than the two mentioned above. A second analysis related to the first one, but with a different point of view, was the one called density visualization (Figure 3). Figure 3: Density visualization. Figure 2: Network visualization. This analysis takes up the keywords considered above, but in this visualization the color represents the density of every item. By default, colors range from blue to green to yellow. The larger the number of items in the neighborhood of a point and the higher the weights of the neighboring items, the closer the color of the point is to yellow. For the other way around, i.e., the smaller the number of items in the neighborhood of a point and the lower the weights of the neighboring items, the closer the color of the point is to blue. Evidence are that, apart from fault detection, fault diagnosis and rolling bearings, all other terms have the same density of occurrence. The extension of the analysis of the weight of words to titles and abstracts extracts a greater number of concepts, application areas, and techniques with relevance to the research area investigated. In the third analysis of Figure 4, it was decided not to consider the words that were already highlighted in the previous analysis. In this way, it was possible to identify several new concepts, more specific than those identified with the keywords. Precisely because of the specificity of these terms, it was decided not to consider the relationships between the words, as done in Figure 3, since it this could lead to unhelpful and misleading considerations. Indeed, considering the thematic breadth, nuances and connections would be lost. In this analysis, specific process parameters can be identified, such as speed, pressure, temperature, or energy. Beyond that, the problem of background noise becomes apparent, related to the concept of the working environment and the non-linear processes. If attention is focused, however, on the concept of fault detection and fault diagnosis, some more peculiar concepts emerge, including: pattern recognition; degradation; class, meaning fault class; degree, meaning the level of gravity of a fault; fault feature; feature vector; fault pattern; fault type; or operating mode. As far as techniques and methods are concerned, the presence of the AUDyC algorithm, the spectral kurtosis, the self-organizing maps, and the intrinsic mode function is revealed. Finally, the concept of real-time application and outlier is identified by the analysis. This analysis is a first investigation of the thematic area, based on the occurrence of terms.