###### Abstract  
In the field of industrial process monitoring, scholars and practitioners are increasing interest in time-varying processes, where different phases are implemented within an unknown time frame. The measurement of process parameters could inform about the health state of the production assets, or products, but only if the measured parameters are coupled with the specific phase identification. A combination of values could be common for one phase and uncommon for another phase; thus, the same combination of values shows a high or low probability depending on the specific phase. The automatic identification of the production phase usually relies on clustering techniques. This is largely due to the difficulty of finding training fault data for supervised models. With these two considerations in mind, this contribution proposes the Latent Dirichlet Allocation as a natural language-processing technique for reviewing the topic of clustering applied in time-varying contexts, in the maintenance field. Thus, the paper presents this innovative methodology to analyze this specific research fields, presenting the step-by-step application and its results, with an overview of the theme. L +
Footnote †: journal: Applied sciences  
is currently an open challenge for research. It is worth noting that processes that operate under time-varying conditions are extremely common in the existing industrial scenario. One example is the pharmaceutical sector, with processes such as granulation or processes involving rotating machines in their many applications. The level of complexity in these engineering systems and manufacturing processes is higher than in stationary situations. Managing the reliability of these systems becomes challenging in modern dynamic operational settings. In particular, the difficulties of monitoring time-varying processes are obvious, because the different variability characteristics present within the same process can lead to monitoring with incorrect results [4], with the consequent failure to identify faults or false alarms [5]. The model tends to confuse in discerning between intrinsic changes in the nature of the process, dictated precisely by its time-variant nature, and real failure situations. Data management for CBM still appears as an immature research area, with many open challenges at different levels, since several CBM approaches are data-driven [6]. Entering into the details of the time-varying conditions, it is possible to assert a significant increase of interest in recent years in the field of fault detection and maintenance in general [7; 8; 9; 10]. As an important remark for the presented research, the application of clustering techniques in the maintenance research gained considerably. The most common motivation for the application of clustering is that supervised learning techniques are not available; in fact, there are many contexts in which access to failure data is limited [11]. The difficulty of monitoring or rare failure situations lead to unavailability of data to feed supervised machine learning models. In this situations, the unsupervised techniques lead to more reliable results compared to supervised learning methods [12]. Clustering is the basic task for unsupervised learning and it helps to overcome data shortage problems [13; 14; 15]. Since the evidence just introduced, the research topic focused on the coexistence of these two themes, namely clustering applied specifically in time-variant contexts. The aim of this review is, therefore, to understand how clustering has, so far, been used to support maintenance in time-variant contexts. Specifically, the purpose is to define the functions that clustering has in this context, the main areas of application and the clustering techniques that have been most investigated and applied. To do this, an approach using the natural language process (NLP) was proposed to support the literature analysis.