## 5 Discussion and Conclusions  
Following the analysis of the four topics, conclusions can be drawn not only on the current state of clustering research, but also on the current role of LDA in this type of analysis. Starting from the analysis of the literature, it is interesting and important to note that, although research is evolving, there are still many scenarios and contexts to be investigated. In detail, although some sectors such as chemicals and petrochemicals present some analyses, there is more of a tendency to analyze specific components rather than industrial processes in their entirety. The main exception to this is batch processes, where clustering tends to identify the sub-phases or operations modes of the process. In general, however, it is possible to say that clustering has mainly the purpose of helping to extract information for further analysis or has the role of a diagnostic tool. The application of the LDA shows that time-variant contexts often present evidence of non-linear trends. This is clear studying the keywords extracted by the LDA algorithm, where the word \"non-linear\" is present in all four topics. The same applies to the term \"fuzzy\", which is present in all four topics; fuzzy clustering encompasses numerous techniques that are highly applied in various contexts. As also mentioned during the contribution, bearings, consistent with maintenance analyses in general, are the most common mechanical element studied in time-varying situations. Another piece of evidence is that k-mean and fuzzy c-means clustering are the most widely used clustering algorithms, due to their widespread use and ease of application compared to other techniques. However, their limitations are also emphasized, and new solutions are proposed. Some further evidence stands out. First, researchers still do not give particular attention to the multi-component systems, which are extremely prevalent in real industrial contexts, and mechanical systems are commonly considered as a single element. In general, except for PCA, multivariate statistical techniques related to clustering are scarcely present. This ties in with the evidence that the contributions present little insights to deal with the process non-linearities. The results of the application of LDA could certainly be considered positive. As mentioned in the introduction phase, some contributions were shifted between the various clusters, but this did not compromise the successful implementation of the LDA. LDA, regardless of the topic allocation, made it possible to have a view of some thematic areas of interest and peculiar techniques, before reading the studies. Based on this, the reading of the papers was certainly more conscious and rational. The reason is two-fold. Firstly, the reading of the papers focuses on the concepts expressed by the keywords of the specific topic, allowing us to identify, for example, correlations between techniques and application contexts that otherwise could have been missed. Similarly, having already extracted the keywords of all the topics, it was easy to identify incorrect allocations, as well as transversal themes between the various topics. In addition, considering a cross-cutting theme such as bearings, it was possible to quickly identify small thematic sub-areas within the larger theme. In addition, the availability of a measure for every content of the contribution to the topic lets the researcher start with those with higher values of belonging. In this way, when the contributions with a lower level of belonging were read, it was possible to identify immediately whether it was necessary to modify their allocation and whether they took up a theme already presented in other read contributions. In general, therefore, the application of LDA should be seen as a valid pre-processing phase for the subsequent literature reviews. The issue of topic clustering is interesting and relevant, but, according to the authors, the most important outcome is the definition of keywords, as a guide for the reading of the contributions.