## 3 Data Extraction and Documentation  
For each publication in the database the following information was extracted: publication title, authors, year, geographical area, research and contribution type, component investigated as per case study, sensor data monitored, task performed, machine learning strategy, and method applied. We designed a code to extract headings from the selected proposal. We implemented the Latent Dirichlet Allocation to analyze text and mine for probabilities of words [42]. For each publication in the database, we read the proposal in order to cluster the strategy and method applied. Each publication was classified as either \"evaluation research\" or \"validation research\" (see Table 3 for details). These categories are further divided into subcategories as per Figure 2.  
Figure 2: Classification of research methods. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Type of Research** & **Description** \\ \hline \multirow{8}{*}{**VALID**} & _Theoretical Research_ presents a vision or a review for a topic. _Research_ encompassing novel and unique _techniques_, not yet used in practise for example experiments, i.e., done in the laboratory. \\ \cline{2-3}  & _Philosophical paper_ provides a conceptual way of looking at a particular problem or field, in the form of a taxonomy or conceptual framework. \\ \cline{2-3}  & _Opinion paper_ expresses a personal interpretation about whether a particular technique is good or bad or how things should been done, without focusing on related work or standard research methods. \\ \cline{2-3}  & _Experience paper_ is written from the personal experience of the researcher, and describes how something has been done in practice. \\ \cline{2-3}  & _Solution proposal_ provides a solution, with a small example or a good line of argumentation, to a particular problem or a significant extension of an existing technique. \\ \cline{2-3}  & _Content Research_ validates a significant implementation of a given technique and takes place in a real-world industrial context, considering benefits and drawbacks of the solution. \\ \cline{2-3}  & _Implementation research_ includes an experiment or a case study. \\ \cline{2-3}  & _Commercial research_ presents a novel solution that is fully commercialized. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Type of research included. Reports containing a survey, case study, controlled experiment, action research, ethnography, simulation, prototyping, and mathematical (software engineering state of art analysis) were included. To classify the type of contribution, the qualitative research method known as key-wording was chosen. The abstract of each publication was reviewed to identify the main keywords, over the declared, that described the research output of the paper. Research labelled as theory or platform [43] was excluded in this phase. Table 4 provides a summary of each type of research contribution. Furthermore, each publication used in the study was classified in the type of contribution. This study sought to highlight the amount of interest, the current maturity level, pertinent trends in the literature, and the focus of the research shown in a new and pervasive research area, as the application of the advanced techniques of machine learning for predictive maintenance in smart manufacturing. In particular, the study presented some machine learning techniques for predictive maintenance to highlight the advantages of their application to improve situational intelligence, performance, and reliability in smart manufacturing. The research questions led the adopted systematic mapping process, to help other researchers in the field to analyze research directions and identify interesting needs and perspectives in the field. The systematic mapping was carried out by gathering high-level knowledge from 78 publications, deemed relevant for the study, according to the aforementioned inclusion/exclusion criteria.