Abstract

Machine learning is a vital part of today's world. In Industry 4.0, Machine Learning approach for Predictive Maintenance continues to generate research attention, especially with the AI implementation. Indeed, the benefits of this approach such as helping determine the condition of equipment and predicting when maintenance should be performed, are extremely strategic. In this article, we propose a new Sensors Reference Model (SRM) and architecture for a Smart Maintenance Simulator (SmaSim) based on a new advanced connected sensors called Smart Sensors. This SmaSim is a low code simulator which can help researchers, engineers, and practitioners to select appropriate Smart Sensors and Machine Learning algorithms for predictive maintenance in Smart Factories.

References

• T. Wagner, C. Herrmann, S. Thiede, “Industry 4.0 Impacts on Lean Production Systems”, Procedia CIRP 63 (2017), pp. 125-131 10.1016/j.procir.2017.02.041
• J. Hull, " The second industrial revolution and the staples frontier in Canada: rethinking knowledge and history", Sci Can, 18 (1) (1994), pp. 22-37
• A. Toffler " The third wave", vol. 484, Bantam books, New York (1980).
• J. Bloem, M. Van Doorn, S. Duivestein, D. Excoffier, R. Maas, E. Van Ommeren, " The fourth industrial revolution: things to tighten the link between IT and OT contents", Groningen Sogeti VINT (2014).
• Mamoona Humayun, “Role of Emerging IoT Big Data and Cloud Computing for Real Time Application”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 4, 2020.
• H. M. Hashemian and W. C. Bean, "State-of-the-art predictive maintenance techniques", IEEE Transactions on Instrumentation and measurement, vol. 60, no. 10, pp. 3480-3492, 2011.
• M. Paolanti, L. Romeo, A. Felicetti, A. Mancini, E. Frontoni and J. Loncarski, "Machine Learning approach for Predictive Maintenance in Industry 4.0," 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), Oulu, Finland, 2018, pp. 1-6, doi: 10.1109/MESA.2018.8449150.
• E. Frontoni, R. Pollini, P. Russo, P. Zingaretti and G. Cerri, "Hdomo: Smart sensor integration for an active and independent longevity of the elderly", Sensors, vol. 17, no. 11, pp. 2610, 2017.
• Carvalho, T.P.; Soares, F.A.; Vita, R.; Francisco, R.D.; Basto, J.P.; Alcalá, S.G. A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 2019, 137, 106024.
• Jovani Dalzochioa, Rafael Kunsta,∗, Edison Pignatonb, Alecio Binottoc, Srijnan Sanyalc, Jose Favillad, Jorge Barbosaa , “Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges” . Comput. Ind. 2020, 123, 103298
• Jovani Dalzochioa, Rafael Kunsta,∗, Edison Pignatonb, Alecio Binottoc, Srijnan Sanyalc, Jose Favillad, Jorge Barbosaa , “Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges” . Comput. Ind. 2020, 123, 103298- Fig. 9
• Jay Lee, Hung-AnKao, Shanhu Yang , "Service innovation and smart analytics for industry 4.0 and big data envirenement" Procedia CIRP 16 ( 2014 ) 3 – 8
• J. Yan, Y. Meng, L. Lu and L. Li, "Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance," in IEEE Access, vol. 5, pp. 23484-23491, 2017, doi: 10.1109/ACCESS.2017.2765544.
• Wo Jae Leea, Haiyue Wua, Huitaek Yunb, Hanjun Kimb, Martin B.G. Junb, John W. Sutherlanda "Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data", Procedia CIRP 80 (2019) 506–511
• G. A. Susto, A. Schirru, S. Pampuri, S. McLoone and A. Beghi, "Machine Learning for Predictive Maintenance: A Multiple Classifier Approach," in IEEE Transactions on Industrial Informatics, vol. 11, no. 3, pp. 812-820, June 2015, doi: 10.1109/TII.2014.2349359.
• N. Amruthnath and T. Gupta, "A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance", 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), 2018, pp. 355-361, doi: 10.1109/IEA.2018.8387124.
• S. Butte, A. R. Prashanth and S. Patil, "Machine Learning Based Predictive Maintenance Strategy: A Super Learning Approach with Deep Neural Networks," 2018 IEEE Workshop on Microelectronics and Electron Devices (WMED), 2018, pp. 1-5, doi: 10.1109/WMED.2018.8360836.
• A. Kanawaday and A. Sane, "Machine learning for predictive maintenance of industrial machines using IoT sensor data," 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), 2017, pp. 87-90, doi: 10.1109/ICSESS.2017.8342870.
• Kalsoom, T.; Ramzan, N.; Ahmed, S.; Ur-Rehman, M. “Advances in Sensor Technologies in the Era of Smart Factory and Industry 4.0”, Sensors 2020, 20(23), 6783.
• EY, Oxford Analytica, “Sensors as drivers of Industry 4.0”, An EY report prepared in collaboration with Oxford Analytica