Abstract
Industry 4.0 is envisioned to apply Cyber-Physical Systems to production systems, assembling lines and other industrial solutions. However, new and innovative approaches may be also designed to solve traditional problems in a more efficient and low-cost manner. In particular, one of the most recognized problems in industry is the prevention of occupational hazards. Many different specific scenarios and risks could be identified in industrial scenarios, but nowadays the industrial sector where (probably) more risks are present is civil works. The use of heavy equipment, where drivers have a limited visual field, is a potential problem for workers at ground level. In this paper it is proposed an Industry 4.0 solution for this situation, where workers are provided with a beaming element, whose signal is received in a central node placed in the machinery. Received signal are processed and analyzed to create alarms and other messages to the driver. The proposed solution is validated in a real scenario using real heavy equipment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bordel, B., Alcarria, R., Robles, T., Martín, D.: Cyber–physical systems: extending pervasive sensing from control theory to the Internet of Things. Pervasive Mob. Comput. 40, 156–184 (2017)
Sánchez, B.B., Alcarria, R., de Rivera, D.S., Sánchez-Picot, A.: Enhancing process control in Industry 4.0 scenarios using cyber-physical systems. JoWUA 7(4), 41–64 (2016)
Bordel, B., de Rivera, D.S., Sánchez-Picot, Á., Robles, T.: Physical processes control in Industry 4.0-based systems: a focus on cyber-physical systems. In: Ubiquitous Computing and Ambient Intelligence, pp. 257–262. Springer, Cham (2016)
Lee, J., Bagheri, B., Kao, H.A.: A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015)
Theorin, A., Bengtsson, K., Provost, J., Lieder, M., Johnsson, C., Lundholm, T., Lennartson, B.: An event-driven manufacturing information system architecture for Industry 4.0. Int. J. Prod. Res. 55(5), 1297–1311 (2017)
Bagheri, B., Yang, S., Kao, H. A., Lee, J.: Cyber-physical systems architecture for self-aware machines in industry 4.0 environment. IFAC PapersOnLine 48(3), 1622–1627 (2015)
Stock, T., Seliger, G.: Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP 40, 536–541 (2016)
Weyer, S., Schmitt, M., Ohmer, M., Gorecky, D.: Towards Industry 4.0-Standardization as the crucial challenge for highly modular, multi-vendor production systems. IFAC Papersonline 48(3), 579–584 (2015)
Lasi, H., Fettke, P., Kemper, H.G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014)
Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., Harnisch, M.: Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries, vol. 9. Boston Consulting Group (2015)
Wang, S., Wan, J., Zhang, D., Li, D., Zhang, C.: Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput. Netw. 101, 158–168 (2016)
Shrouf, F., Ordieres, J., Miragliotta, G.: Smart factories in Industry 4.0: a review of the concept and of energy management approached in production based on the Internet of Things paradigm. In: 2014 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 697–701. IEEE (2014)
Lee, J., Kao, H.A., Yang, S.: Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP 16, 3–8 (2014)
Gorecky, D., Schmitt, M., Loskyll, M., Zühlke, D.: Human-machine-interaction in the Industry 4.0 era. In: 2014 12th IEEE International Conference on Industrial Informatics (INDIN), pp. 289–294. IEEE (2014)
Kolberg, D., Zühlke, D.: Lean automation enabled by industry 4.0 technologies. IFAC-PapersOnLine 48(3), 1870–1875 (2015)
Wan, J., Tang, S., Shu, Z., Li, D., Wang, S., Imran, M., Vasilakos, A.V.: Software-defined industrial internet of things in the context of industry 4.0. IEEE Sensors J. 16(20), 7373–7380 (2016)
Wootton, C.: Samsung ARTIK Reference: The Definitive Developers Guide. Apress, Berkeley (2016)
Schelkunoff, S.A., Friis, H.T.: Antennas: theory and practice, vol. 639. Wiley, New York (1952)
Bordel, B., Alcarria, R., Robles, T., Sánchez-Picot, Á.: Stochastic and information theory techniques to reduce large datasets and detect cyberattacks in Ambient Intelligence Environments. IEEE Access 6, 34896–34910 (2018)
Acknowledgments
The research leading to these results has received funding from the Ministry of Economy and Competitiveness through SEMOLA project (TEC2015-68284-R), from the Centre for the Development of Industrial Technology (CDTI) through PERIMETER SECURITY project (ITC-20161228) and from the Autonomous Region of Madrid through MOSI-AGIL-CM project (grant P2013/ICE-3019, co-funded by EU Structural Funds FSE and FEDER).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bordel, B., Alcarria, R., Robles, T., González, D. (2019). An Industry 4.0 Solution for the Detection of Dangerous Situations in Civil Work Scenarios. In: Rocha, Á., Ferrás, C., Paredes, M. (eds) Information Technology and Systems. ICITS 2019. Advances in Intelligent Systems and Computing, vol 918. Springer, Cham. https://doi.org/10.1007/978-3-030-11890-7_48
Download citation
DOI: https://doi.org/10.1007/978-3-030-11890-7_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11889-1
Online ISBN: 978-3-030-11890-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)