Requirements for an Intelligent Maintenance System for Industry 4.0

comprobación paso "titulo publicación " - Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future[EN] Recent advances in the development of technological devices and software for Industry 4.0 have pushed a change in the maintenance management systems and processes. Nowadays, in order to maintain a company competitive, a computerised management system is required to help in its maintenance tasks. This paper presents an analysis of the complexities and requirements for maintenance of Industry 4.0. It focuses on intelligent systems that can help to improve the intelligent management of maintenance. Finally, it presents a summary of lessons learned specified as guidelines for the design of such intelligent systems that can be applied horizontally to any company in the Industry.This work is supported by the FEDER/Ministry of Science, Innovation and Universities - State Research Agency RTC-2017-6401-7Garcia, E.; Araujo, A.; Palanca Cámara, J.; Giret Boggino, AS.; Julian Inglada, VJ.; Botti, V. (2019). Requirements for an Intelligent Maintenance System for Industry 4.0. Springer. 340-351. https://doi.org/10.1007/978-3-030-27477-1_26S340351CEN, European Committee for Standardization: EN 13306:2017. Maintenance Terminology. European Standard (2017)Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., Yin, B.: Smart factory of Industry 4.0: key technologies, application case, and challenges. IEEE Access 6, 6505–6519 (2018). https://doi.org/10.1109/access.2017.2783682Crespo Marquez, A., Gupta, J.N.: Contemporary maintenance management: process, framework and supporting pillars. Omega 34(3), 313–326 (2006). https://doi.org/10.1016/j.omega.2004.11.003Ferreira, L.L., Albano, M., Silva, J., Martinho, D., Marreiros, G., di Orio, G., Malo, P., Ferreira, H.: A pilot for proactive maintenance in Industry 4.0. In: 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS). IEEE (2017). https://doi.org/10.1109/wfcs.2017.7991952Goh, K., Tjahjono, B., Baines, T., Subramaniam, S.: A review of research in manufacturing prognostics. In: 2006 IEEE International Conference on Industrial Informatics, Singapore, pp. 417–422. IEEE (2006). https://doi.org/10.1109/INDIN.2006.275836Hashemian, H.M., Bean, W.C.: State-of-the-art predictive maintenance techniques. IEEE Trans. Instrum. Meas. 60(10), 3480–3492 (2011). https://doi.org/10.1109/TIM.2009.2036347Lee, W.J., Wu, H., Yun, H., Kim, H., Jun, M.B., Sutheralnd, J.W.: Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia CIRP 80, 506–511 (2019)Lu, B., Durocher, D., Stemper, P.: Predictive maintenance techniques. IEEE Ind. Appl. Mag. 15(6), 52–60 (2009). https://doi.org/10.1109/MIAS.2009.934444Mrugalska, B., Wyrwicka, M.K.: Towards lean production in Industry 4.0. Procedia Eng. 182, 466–473 (2017). https://doi.org/10.1016/j.proeng.2017.03.135O’Donoghue, C., Prendergast, J.: Implementation and benefits of introducing a computerised maintenance management system into a textile manufacturing company. J. Mater. Process. Technol. 153, 226–232 (2004)Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., Loncarski, J.: Machine learning approach for predictive maintenance in Industry 4.0. In: 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA). IEEE (2018). https://doi.org/10.1109/mesa.2018.8449150Patil, R.B., Mhamane, D.A., Kothavale, P.B., Kothavale, B.: Fault tree analysis: a case study from machine tool industry. Available at SSRN 3382241 (2018)Potes Ruiz, P.A., Kamsu-Foguem, B., Noyes, D.: Knowledge reuse integrating the collaboration from experts in industrial maintenance management. Knowl. Based Syst. 50, 171–186 (2013). https://doi.org/10.1016/j.knosys.2013.06.005Razmi-Farooji, A., Kropsu-Vehkaperä, H., Härkönen, J., Haapasalo, H.: Advantages and potential challenges of data management in e-maintenance. J. Qual. Maint. Eng. (2019)Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Harnisch, M.: Industry 4.0: the future of productivity and growth in manufacturing industries. Boston Consult. Group 9(1), 54–89 (2015)Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., Vasilakos, A.V.: A manufacturing big data solution for active preventive maintenance. IEEE Trans. Ind. Inform. 13(4), 2039–2047 (2017). https://doi.org/10.1109/tii.2017.267050

A collaborative machine tool maintenance planning system based on content management technologies

From product maintenance and service point of view, high-value sophisticated computer numerical control (CNC) machine tools in modern manufacturing factories play important roles: they are manufacturing equipment, on the other hand, they are also products supplied by equipment manufacturers. There is a trend that manufacturers are extending their responsibilities to the products use phase to meet customers’ requirements for life-time support and service. To ensure the effective performance and efficient maintenance of high-value machine tools, information and knowledge from their lifecycle should be collected and reused. However, in the research area of product service systems and related computerised maintenance systems, there is a lack of research work on how to integrate knowledge from different stakeholders into the maintenance and service planning process, which is important for modern digital manufacturing systems to reduce machine tools’ downtime and improve their working performance. This project proposed a collaborative maintenance planning framework to connect different stakeholders and integrate their knowledge into the maintenance and service process. The potential of advanced content management systems (CMS), which are widely used non-engineering sectors such as finance, business, publishing and government organizations, has been explored and tested for applications in the manufacturing engineering domain. The research realised that CMS have several advantages compared with traditional engineering information systems, especially in managing dynamic and unstructured knowledge. A prototype maintenance and service planning system has been developed and evaluated using a real CNC machine tool