Diseño de un Modelo Predictivo en el Contexto Industria 4.0


The Internet of Things (IoT), the development and installation of advanced sensors for data collection, computer solutions for remote connection and other disruptive technologies are marking a transformation process in the industry; giving rise to what various sectors have called the fourth industrial revolution or Industry 4.0. With this process of change, organizations face both new opportunities and challenges. This article focuses on the modeling and integration of industrial data, generated by sensors installed in machines. The extraction of patterns is proposed, using data fusion techniques that allow the design of a predictive maintenance model. Finally, a case study is presented with a database that is applied to the Naive Bayes Algorithm to obtain predictions.

Keywords: Industry 4.0, Sensors, Internet of Things, Pattern Extraction, Omnibus Models. 

[1] Almasri, M. M., and Elleithy, K. M. (2014). “Data fusion models in WSNs: comparison and analysis”. In American Society for Engineering Education (ASEE Zone 1), 2014 (203) Zone 1 Conference of the IEEE, pp 1-6, doi:10.1109/ASEEZone1.2014.6820642.

[2] Al Momani, B.; Morrow, P.; McClean, S. (2011). “Fusion of Elevation Data into Satellite Image Classification Using Refined Production Rules. In Image Analysis and Recognition”. 8 th International Conference, ICIAR 2011, Burnaby, Canada, Junio 22-24, 2011. Proceedings, Part I, Ed. Springer: Berlin/Heidelberg, Germany; pp. 211–220, doi: 10.1007/978-3-642-21593-3_22.

[3] Bajo J., De Paz J. F., Villarrubia G., and Corchado, J.M. (2015). “Self-organizing architecture for information fusion in distributed sensor networks,” Int. Journal Distrib. Sens. Networks, vol. 11, pp.1-13, doi: 10.1155/2015/231073.

[4] Ballesteros, F. (2017). “La Estrategia Predictiva en el mantenimiento industrial”. Predictécnico (Vol. 23), pp. 36-45. Grupo Álava, España.

[5] Bedworth, M., & O’Brien, J. (2000). “The Omnibus model: a new model of data fusion?”. IEEE Aerospace and Electronic Systems Magazine, vol. 15(Issue 4), pp. 30-36, doi:10.1109/62.839632

[6] Bishop, C.M., (2006). “Pattern recognition and machine learning”. Springer, New York, Vol. 4, doi:10.117/1.2819119.

[7] Civerchia, F., Bocchino, S., Salvadori, C., Rossi, E., Maggiani, L., and Petracca, M. (2017). Industrial Internet of Things Monitoring Solution for Advanced Predictive Maintenance Applications. Journal of Industrial Information Integration, doi: 10.1016/j.jii.2017.02.003.

[8] Cruz, M., Oliete, P., Morales, C., González, C., Cendón, B., & Hernández, A. (2015). ”Las Tecnologías IoT dentro de la Industria Conectada 4.0”. Gobierno de España, Ministerio de Industria, Energía y Turismo, Escuela de Organización Industrial (eoi). Libro digital en: http://a.eoi.es/industria4, 20/4/2017 (última consulta).

[9] Gobierno de España, Ministerio de Industria, Energía y Turismo, Santander y Telefónica (2015). “La Transformación Digital de la Industria Española. Informe Preliminar”. http://www6.mityc.es/IndustriaConectada40/ informe-industria-conectada40.pdf, 15/5/2017 (última consulta).

[10] Hortonworks, (2017). “Analyze HVAC Machine and sensor data”. https://es.hortonworks.com/hadoop-tutorial/how-to-analyze-machine-andsensor-data/#section-2. 1/6/2017 (última consulta)

[11] Kagerman, H., Anderl, R., Gausemeier J., Schuh G., and Wahlster W. (2016). “Industrie 4.0 in a Global Context: Strategies for Cooperating with International Partners”, Acatech Study, Munich, Germany. https:// www.acatech, 26/5/2017 (última consulta).

[12] Siciliano, B., Oussama, K. (2008). “Handbook of Robotic”. Ed. Springer: Berlin/Heidelberg, Germany, doi: 10.1007/978-3-540-30301-5, pp:1611.