## 4 Results  
The systematic map shown in Figure 5 was used to select relevant papers in the field of analysis. A total of 483 publications were originally selected using keywords and main sources as per RQs.  
Basing on exclusion criteria I, 205 papers were considered out from the domain of search. The remaining papers were then submitted to keywords screening test, according to a Latent Dirichlet allocation analysis of text in the abstract and conclusion. A total of 82 papers were then rejected basing on exclusion criteria II. They resulted in 67 articles of relevance matching. The snowballing method was finally used to extract the references from each of these publications. We screened referenced papers to conclude about inclusion or rejection in the database. Finally, 78 relevant papers constituted the database for answering RQs and to conclude about the role of ML techniques for PdM. The references paper are available in the Table A1.  
**RQ1.How many articles cover the use of machine learning for predictive maintenance in smart manufacturing?**  
An upward trend was observed in publications between 2013 and 2018 (Figure 6). A quite stable trend was revealed in the published literature about ML for PdM before 2016. Since 2017, an increase in interest in research about the topic was apparent with a fivefold increase noted in 2017, and ever-increasing output continuing in 2018 and early months of 2019.  
Figure 5: A breakdown of the screening process.  
Figure 7 further analysed this trend by differentiating between ML and DL terms. Papers were highlighted by focusing on the DL approach or those applying DL methods. The growth in publications was especially registered in the last two years, after a downfall in 2016, and in 2017-2018 their number was exactly one-third of the total.  
An interesting result relates to the provenance map of publication (from the first author location) as displayed in Figure 8. The highest number of publications about ML for PdM was from USA, with 21 papers, then China with 13 papers, Italy 8, Germany 7, India 6, Sweden and Spain 4, and U.K. 3. A single paper was published from each of Turkey, Ireland, Australia, Taiwan, Korea, Kazakhstan, Switzerland, Bulgaria, Brazil, Israel, Greece, Japan, Mexico, Norwegian, Serbia, Thailand, and Austria.  
**RQ2.What type of research is being conducted in this area of ML techniques per PdM in SM?**  
Figure 9 reports on the type of research. There is a perfect sub-division of the analysed papers between evaluation-based proposal and validation reports. Evaluation is mainly based on an advanced research approach that is trying to explore consequences, benefits, and drawbacks within the industrial case test. The 39 validation reports provided, generally, conceptual analysis and solutions over a simulation test mainly based on open source data sets.  
Figure 6: Publications over the years.  
Figure 7: Trend in deep learning publications.  
Figure 10 displays the distribution of the type of research. In 2015, 2016, and 2017 there was a prevalence of validation-based studies. In 2018 a significant rise in evaluation proposal was reported as consequence of the presence real industrial applications.  
Figure 8: Geographical provenance of publications—evaluation and validation as per main analysis.  
Figure 10: Popularity of research types by year (stable reports until early 2019).  
Figure 9: Presence of research types.