## 5 Discussion  
### RQ1  
RQ1 wants to assess the level of interest in machine learning techniques for predictive maintenance. At present, thanks to technological innovation, ML4 PdM reported an exponential growth in publications over the last 5 years. The years 2007-2016 registered the following: the economic collapse of the oil market for Japan and Russia; Italy struggled with problems with banks and internal debt; an economic crisis affected Spain; Australia and U.K. manufacturing sectors declined; and in 2015-2016 China's stock market crashed. It included the market turbulence, which culminated in the devaluation of the yuan, slowing growth in the GDP of China, a fall in petroleum prices. Moreover, Greece was faced with the default in June 2015; October 2014 registered the effects of the end of quantitative easing in the United States and a sharp rise in bond yields in early 2016. Finally, in June 2016, there was the United Kingdom European Union membership referendum, in which Brexit was voted upon. Dangerous trends that have manifested themselves in the world economy include falling prices of raw materials, double-digit competitive devaluations in emerging countries, and consequent massive flight of capital [94]. These economic collapses confirmed by the interest in restructuring the cost components while using artificial intelligence (AI), big data, and analytic in deep learning (40% of the analysed publications) arrangement for predictive and self-teaching systems. It was also observed that the USA and China are showing leadership in publications. The potential reason for this could be the policy initiatives in these countries, i.e., Smart Manufacturing Leadership Coalition (SMLC) and China 2025. SMLC is a national non-profit focused on transforming U.S. manufacturing through democratizing access to real-time data. SMLC is proposing a U.S. Public-Private Partnership Program and \"Framework for Revitalizing American Manufacturing\". That provides a comprehensive approach to transform manufacturing plants, their supply chains including of education and knowledge, transportation, and stable capital markets. More in detail, SMLC is developing the nation's first open platform and marketplace for real-time data analytics and industrial competition for manufacturing. This infrastructure will enable manufacturers to access real-time data for strategic decisions. The architecture is not an Internet of Things, but an Internet of Manufacturing that allows solutions to be connected and applied through an open framework. On the other side, \"CHINA 2025\" is the oriental  
Figure 21: Relation between machine learning methods and type of investigated equipment. response to the European \"Industry 4.0\". It is the attempt to promote manufacturing innovation in China. The seven-step program of \"CHINA 2025\" focuses on artificial intelligence, biotechnology, and robotics. Its \"Made in China 2025\" strategy aims at mastering design, software, and production, building the right pool of talent. It targets a combination of high-tech sectors and infrastructure. It focuses on building powerful and competitive industries, building stable legal and economic institutions, maintaining a sustainable macro environment, establishing the right partnership, and division of responsibilities between private and public sectors. **RQ2. **  
RQ2 arose from the need to assess the maturity level of ML4 PdM. The prevalence of validation research approach, based on theoretical models, with no details in test beds and implementation, would have been synonymous with immature research area. This would be one which requires the development of theory to support forthcoming applications. On the contrary, most of the studies reviewed related to real industry test beds. This is indicative of a research area at an already promising level.