Miller Magazine Issue: 127 July 2020

55 COVER STORY MILLER / JULY 2020 centralized decision-making”. PdM drastically improves technical support by catching errors that humans cannot see [8]. Especially if based on machine learning, PdM decisions are based only on data so that centralized decision making is eliminated. This may not be always acceptable in all types of establishments in earlier steps. PdM plays a key role in Industry 4.0. With its help, “manufacturers will have full lifespan use of parts and no unplanned downtime will be faced”. This means fa- cilitating smart factories that enable machine to machine learning improving at the end safety and productivity 8 . Additionally, if the technologies enabling PdM are ex- plored, one can see that those technologies also con- tribute to digital transformation which is necessary for an Industry 4.0 plant. Figure 3 gives a table of technol- ogies that enable PdM 9 . Improving effect of Industry 4.0 on Predictive Maintenance In real applications, impacts of Industry 4.0 and PdM on each other cannot be separated actually. There is a loop which is always repeating itself after having closed, resulting a continuous development fed by the impact of PdM on Industry 4.0, and vice versa. In other words, PdM fits into Industry 4.0 as Internet of Things (IoT) the main element of Industry 4.0 is a crucial enabler for it 8 . Continuous data from live sensors, and intelligent systems analysing that data are crucial for PdM. This is just the essential point to focus. The more Indus- try 4.0 tools are implemented, the higher will be the performance of PdM. Industry 4.0 is shortly defined also as “the superposition of several technological developments related to Cyber Physical Systems (CBS), Internet of Things (IoT), Internet of Services (IoS), and Data Mining (DM)” 4 . Such a superposi- tion will provide ideal data feed to PdM for estimat- ing, monitoring and controlling of eventual failures and inconsistencies. Industry 4.0 enables to realize the “smart factory” idea, and smart factories can lead even to “self-aware and self-maintained machine systems which can self-as- sess its own health and degradation, and use also in- formation from other peers for smart maintenance de- cisions” 4 , which can be considered as an exact fit for industrial big data environment. For mechanical systems, self-awareness means “being able to assess the current or past condition of a machine, and react to the assessment output 10 . The smarter the factory is, the higher will be the self-awareness of the ma- chines, and smartness level is proportional to implementa- tion and application percentage of Industry 4.0 tools. Mutual interaction of Industry 4.0 and PdM is open to improvements and development as digital transfor- mation has not reached its limits. Tools such as big data environment and cloud computing environment are considered to further facilitate Industry 4.0. Similarly, to facilitate the interaction, studies and applications to further increase the power of PdM are also presented recently. Such a tool attracting attention is Smart Predictive Maintenance (SPdM) discussed widely in production environments. SPdM is a modern maintenance strategy beyond PdM, and incorporates PdM with several tech- nologies and maintenance systems (e.g. CMMS, ERP, MES). Its main feature is the capability to provide also information on maintenance planning, spare parts plan- ning, and automation of maintenance tasks 11 . We conclude shortly that Industry 4.0 promotes PdM and smart manufacturing as machines are connected as a collaborative community in smart factories generating a great potential for PdM 4 . Transition to Industry 4.0 is a dynamic process and continues. PdM plays a funda- mental role in that transition, benefitting also from the developments of that transition in terms of data collec- tion and data analysis. References 1 Alfin, F; Mill Maintenance Methods, Miller Maga- zine, No.101, May 2018 2 Yalçınkaya, A; Etkin Bakım Sistemi, El Kitabı/Kılavuz, AB Holding, Konya, Mayıs 2013 3 Orhan, İ; Karakoç Hikmet; Bakım Yönetim Süreçleri ve Etkinliğin Değerlendirilmesi, Mühendis ve Makina, Sayı 607, Ağustos 2010 4 Li, Z; Wang, K; He, Y;Industry 4.0-Potentials for Pre- dictive Maintenance, Proceedings of the 6th Interna- tional Workshop of Advanced Manufacturing and Auto- mation (IWAMA), October 2016 5 Chesworth, D; Industry 4.0 Techniques as a Main- tenance Strategy (A Review Paper), www.researchgate. net/publication/322369285 Access: 9 June 2020 6 Why Predictive Maintenance is Driving Industry 4.0, Smart Industry, White Paper, Seebo Interactive Ltd, 2018, www.seebo.com/iot-resources, Access: 9 June 2020 7 Becks, A; Machine Learning als Kern der praedika- tiven Wartung, IT & Production, Oktober 2017 8 Roubaud, J; How Predictive Maintenance Fits into Industry 4.0, www.engineering.com, Access: 9 June 2020 9 Coleman, Ch; Damodaran, S; Chandramouli, M; Deuel, E; Making maintenance smarter, Deloitte Uni- versity Press, Deloitte Development LLC, 2017. 10 Lee, J; Kao, H-A; Yang, S; Service innovation and smart analytics for Industry 4.0 and big data environ- ment, Proceedings of the 6th Conference on Industrial Product-Service Systems, Procedia CIRP 16 (2014). 11 Durmuş, M; Smart Predictive Maintenance: Der Schlüssel zu Industrie 4.0 www.aisoma.de , Access: 6 May 2018, and 9 June 2020.