Within Industry 4.0, Prognostics and Health Management (PHM) holds great potential due to its ability to bring deep insights into the current state of manufacturing equipment. When developing PHM competences in higher education, it is desirable to train students in the design and utilization of the algorithms commonly adopted for PHM analyses. However, the unavailability of a widespread big data platform to standardize the data format and easily access sensor data complicates this purpose. To cope with this, XRepo 2.0 is introduced in this work: a big data information system that allows professors to share PHM sensor data in a standard format within an experimental and educational context. To enable the management of the large amount of data available today, the presented information system is designed and implemented by integrating the Hadoop framework with a document database. Moreover, teachers can pre-process the data on the cloud infrastructure, which is a crucial aspect for the assessment of the algorithms developed by the students. Finally, a prototype of XRepo 2.0 has been deployed on the Azure Cloud and validated with respect to functionality and performance criteria. Given the importance of PHM within Industry 4.0, we expect that XRepo 2.0 contributes to the unification and sharing of selected sensor data with the academic community for the development of competences in PHM.
Education, Prognostics and Health Management, Information System, Big Data, Hadoop, MapReduce
Ardila, A., Martinez, F., Garces, K., Barbieri, G., Sanchez-Londono, D., Caielli, A., . . . Fumagalli, L. (2020). XRepo - Towards an information system for prognostics and health management analysis. Procedia Manufacturing, 42, 146–153.
Azure. (2020). Azure ai guide for predictive maintenance solutions. Retrieved 2020-09-28, from https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/predictive-maintenance-playbook
Barbieri, G., Sanchez-Londono, D., Cattaneo, L., Fumagalli, L., & Romero, D. (2020). A Case Study for Problem-based Learning Education in Fault Diagnosis Assessment. IFAC-PapersOnline.
Ben-Daya, M., Kumar, U., & Murthy, D. P. (2016). Introduction to maintenance engineering: modelling, optimization and management. John Wiley & Sons.
Canizo, M., Onieva, E., Conde, A., Charramendieta, S., & Trujillo, S. (2017). Real-time predictive maintenance for wind turbines using big data frameworks. In 2017 ieee international conference on prognostics and health management (icphm) (pp. 70–77).
Cerrada, M., Sánchez, R.-V., Li, C., Pacheco, F., Cabrera, D., de Oliveira, J. V., & Vásquez, R. E. (2018). A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems and Signal Processing, 99, 169–196.
Condie, T., Conway, N., Alvaro, P., Hellerstein, J. M., Elmeleegy, K., & Sears, R. (2010). Mapreduce online. In Nsdi (Vol. 10, p. 20).
Djurdjanovic, D., Lee, J., & Ni, J. (2003). Watchdog agent—an infotronics-based prognostics approach for product performance degradation assessment and prediction. Advanced Engineering Informatics, 17(3-4), 109–125.
Ebeling, C. E. (2004). An introduction to reliability and maintainability engineering. Tata McGraw-Hill Education.
Galar, D., & Kans, M. (2017). The impact of maintenance 4.0 and big data analytics within strategic asset management. In Maintenance performance and measurement and management (mpmm).
Garcia Martinez, M., & Walton, B. (2014). The wisdom of crowds: The potential of online communities as a tool for data analysis. Technovation, 34(4), 203–214.
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.
Kans, M., Campos, J., & H°akansson, L. (2020). A remote laboratory for Maintenance 4.0 training and education.
Kim, N.-H., An, D., & Choi, J.-H. (2016). Prognostics and health management of engineering systems: An introduction. Springer.
Kotu, V., & Deshpande, B. (2014). Predictive analytics and data mining: concepts and practice with rapidminer. Morgan Kaufmann.
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFACPapersOnLine, 51(11), 1016–1022.
Lebold, M., & Byington, C. S. (2002). OSA-CBM architecture development with emphasis on XML implementations. Maintenance and Reliability Conference (MARCON).
Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia Cirp, 38, 3–7.
Lee, J., Lapira, E., Bagheri, B., & Kao, H.-A. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1), 38–41.
Lee, J., Lapira, E., Yang, S., & Kao, A. (2013). Predictive manufacturing system - Trends of next-generation production systems. In Ifac proceedings volumes (Vol. 46, pp. 150–156).
Lee, J., Qiu, H., Yu, G., Lin, J., & Services, R. T. (2007). NASA Ames Prognostics Data Repository. IMS, University of Cincinnati.
Li, R., Verhagen, W. J., & Curran, R. (2020). A systematic methodology for Prognostic and Health Management system architecture definition. Reliability Engineering and System Safety, 193.
Loparo, K. (2012). Bearings vibration data set (Tech. Rep.). The Case Western Reserve University Bearing Data Center.
Martin, K. (1994). A review by discussion of condition monitoring and fault diagnosis in machine tools. International Journal of Machine Tools and Manufacture, 34(4), 527–551.
Mobley, R. K. (2002). An introduction to predictive maintenance. Elsevier.
Moubray, J. (2001). Reliability-centered maintenance. Industrial Press Inc.
MTConnect Institute. (2019). Part 3.0 - Streams Information Model. Retrieved from https://www.mtconnect.org/standard-download20181
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N., & Varnier, C. (2012). PRONOSTIA: An experimental platform for bearings accelerated degradation tests. In Ieee international conference on prognostics and health management.
O’Donovan, P., Leahy, K., Bruton, K., & O’Sullivan, D. T. (2015). An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. Journal of Big Data, 2(1), 1–26.
Open Geospatial Consortium. (2020). Observations and Measurements 2.0 Examples. Retrieved 2020-04-20, from http://schemas.opengis.net/om/2.0/examples/
Redelinghuys, A., Basson, A. H., & Kruger, K. (2019). A Six-Layer Digital Twin Architecture for a Manufacturing Cell. Service Orientation in Holonic and Multi-Agent Manufacturing, 1, 273-284.
Sahal, R., Breslin, J. G., & Ali, M. I. (2020). Big data and stream processing platforms for industry 4.0 requirements mapping for a predictive maintenance use case. Journal of Manufacturing Systems, 54, 138–151.
Schroeder, G. N., Steinmetz, C., Pereira, C. E., & Espindola, D. B. (2016). Digital Twin Data Modeling with AutomationML and a Communication Methodology for Data Exchange. IFAC-PapersOnLine, 49(30), 12–17.
Sezer, E., Romero, D., Guedea, F., Macchi, M., & Emmanouilidis, C. (2018). An Industry 4.0-Enabled Low Cost Predictive Maintenance Approach for SMEs. In Ieee international conference on engineering, technology and innovation (ice/itmc).
Von Birgelen, A., Buratti, D., Mager, J., & Niggemann, O. (2018). Self-Organizing Maps for Anomaly Localization and Predictive Maintenance in Cyber-Physical Production Systems. In Procedia cirp (Vol. 72, pp. 480–485).
Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017). A manufacturing big data solution for active preventive maintenance. IEEE Transactions on Industrial Informatics, 13(4), 2039–2047.
Wang,W., Fan, L., Huang, P.,&Li, H. (2019, 12). A new data processing architecture for multi-scenario applications in aviation manufacturing. IEEE Access, 54, 83637–83650.
Yu,W., Dillon, T., Mostafa, F., Rahayu,W., & Liu, Y. (2020). A global manufacturing big data ecosystem for fault detection in predictive maintenance. IEEE Transactions on Industrial Informatics, 16(1), 183–192.