## 2 Methodology  
The literature review considered in this study was carried out on the Scopus database, the largest abstracts and citations database of peer-reviewed literature [20]. The review has no objective of systematicity, e.g., it does not want to follow the Prisma methodology, because the paper wants to show the results of a review based on NLP. The selected key-search was set targeting a high level of inclusiveness, with regards to the topic. The key-search is (\"cluster\"\" and (\"condition based maintenance\" OR \"fault\"' OR \"useful life\" OR \"diagnos\" OR \"prognos\" OR \"failure\") and (\"time var\" OR \"non stationary\" OR \"multi phase\" OR \"multiphase\" OR \"process phase\")) in the title, abstract, or keywords fields of the database. The final query has been run-up in September 2021, to present analysis as completely as possible, although the topic was monitored in the last year of research. The choice of keywords was dictated by some specific considerations. Firstly, the term \"time var\" was included because the authors wanted to consider contributions that explicitly considered this type of feature in their analyses. In fact, some time-variant processes are occasionally not treated as such, and these contributions were not of interest for this analysis. It has also been noted that the term \"non-stationary\" is often used to describe the characteristics of signals from time-varying contexts [21]. The terms \"multi phase\", \"multiphase\" and \"process phase\" were inserted to investigate the context of multi-phase processes. The term \"cluster\" was inserted to identify those contributions that explicitly referred to clustering as a tool used in the contribution, just as the terms \"condition based maintenance\" OR \"fault\" OR \"useful life\" OR \"diagnos\" OR \"prognos\" OR \"failure\" were inserted to circumscribe the field to the field of maintenance. Subsequently, only contributions in English were selected and following a reading of the abstracts, only contributions with a specific relevance and belonging to the topic were selected. The final analysis was therefore carried out on 72 contributions. The reading of the contributions and their analysis was carried out by all authors of the paper, first separately and then jointly. Figure 1 presents a clear step-by-step representation of the literature analysis developed in the paper. Before proceeding to the actual application of the LDA and the subsequent literature reviews, some graphical analyses were implemented to extract initial information. To do this, the authors made use of VOSviewer, a software tool for constructing and visualizing bibliometric networks. In detail, three analyses were implemented. First, a so-called network visualization was carried out, based on the keywords in the contributions (Figure 2). In this type of visualization, items are represented by their label and a circle. The size of the label and the circle represent the weight of the item, meaning that the higher the weight of an item, the larger the label and the circle of the item, while the lines connecting the circles represent a link. In this specific case, the weight is the occurrence of the keyword within the contributions, considering only those that appeared in at least three contributions. This threshold is needed to make the analysis readable by eliminating keywords used only in specific contributions. This is an outline analysis, while the details of the contributions will be shown in the following analysis. Finally, the color of an item is determined by the cluster to which the item belongs. This analysis makes it possible to identify quickly which are the main concepts in the thematic area, as well as the relationships between the words identified. The relationship between the keywords shows four groups of keywords, defined according to their co-occurrence in the documents.