## 5 Discussion
### Mitigation of Threats to Validity  
The process of systematic mapping is not infallible, and there are indeed a number of threats to the validity of this study. Possible threats for validity are, for example, publication bias, as negative or new and controversial views may not be published (theoretical validity); poorly designed data extraction forms and recording of data (descriptive validity); potential researcher bias in the selection of studies and reporting of the data (theoretical validity); the quality of the sample of studies obtained with respect to the targeted population (theoretical validity); generalizability of the results of the mapping, which includes within the population (internal generalizability) and between different populations (external generalizability); the reliability of the conclusions drawn in relation to the data collected, e.g., due to a possible bias of the researchers in the interpretation of that data [9]. To this end, every effort was made to mitigate potential risks throughout the process. The study was prone to threats in all stages, prevention of which are described below.  
The choices relating to the search criteria were driven by the agreed scope of the research, the research questions that needed to be answered, the PICO criteria, as well as the relevance of papers returned from testing various search combinations. However, there could be the risk that these have restricted the discovery of papers that did not meet the search criteria. Although based on the sophistication of the search facilities in modern digital databases and the competence and experience of researchers in the field with which we were confronted, we could suggest that the risk of omitting relevant papers was at least minimised. Digital databases were selected using prior knowledge relating to engineering and technology research, as well as noting prominent databases used in closely related fields; even if not all digital repositories were included in the study, snowballing was conducted, and there is also a realistic chance that different types of digital repositories would have provided a level of redundancy. While other databases enabled the construction of searches to interrogate titles, abstracts, and keywords using Boolean logic (as concerned Google Scholar), specifying an appropriate search string was necessary, limiting the search to titles that including the following combinations of keywords: \"Smart\" AND \"Machine Learning\" AND \"Predictive\".  
This has returned a manageable number of publications. The criteria defined for inclusion and exclusion in this study were deemed to be aligned with the scope of the study. The main search terms chosen were restricted to the area of interest, but many synonymous terms were considered acceptable. Irrelevant papers were scanned thoroughly before rejecting them. To avoid uncertainty or rejection of relevant papers by only scanning ambiguous abstracts and conclusions, papers were read fully and checked twice to ensure reliability. While scanning papers, extracting incomplete information was prevented by simultaneously building a detailed schematic structure in Excel, related to the research questions, of each paper. The results of the classification process and conclusions included research efforts focusing on systematic mapping. The research presented in this paper provided a broad first review of research relating to the advanced use in smart manufacturing of machine learning techniques for predictive maintenance. This study aims to promote a better understanding of a new and pervasive area that brings many benefits to the industrial environment. In particular, several fundamental research questions that are relevant to current efforts were answered, while also providing a platform for further research and investigation in this field. For any empirical study the discussion of validity threats is of importance and is a quality criterion for study selection.