Abstract
Predictive maintenance is regarded by many as a key factor in Industrial Internet of Things (IIoT) and the development of “smart” factories. With the growing use of sensors and embedded computing systems, the term predictive maintenance is most often understood as a strategy that relies on collecting streaming sensor data and performing condition monitoring. Thus, the majority of academic papers base their research work solely on sensorial sources coming from the shop floor machinery, neglecting the knowledge already existing in legacy systems and maintenance historical logs. The UPTIME project aims to develop a unified predictive maintenance framework that incorporates information from heterogeneous data sources, both from sensor devices and legacy/operational systems. In this contribution, we share our first insights on legacy data analytics in the predictive maintenance context, and outline the tools and approaches we developed in the course of the project. Experimental work has been conducted using real world datasets deriving from an actual manufacturing facility in the White Goods/Home Appliances sector. The results provide significant knowledge about the manufacturing processes and show the potential of the proposed methodology.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
[online] http://www.mimosa.org/.
References
Choudhary, A.K., Harding, J.A., Tiwari, M.K.: Data mining in manufacturing: a review based on the kind of knowledge. J. Intell. Manuf. 20(5), 501 (2009)
Harding, J.A., Shahbaz, M., Srinivas, Kusiak, A.: Data mining in manufacturing: a review. J. Manuf. Sci. Eng. Trans. ASME 128(4), 969–976 (2006)
Kobbacy, K.A.H., Fawzi, B.B., Percy, D.F., Ascher, H.E.: A full history proportional hazards model for preventive maintenance scheduling. Qual. Reliab. Eng. Int. 13(4), 187–198 (1997)
Lin, C.C., Tseng, H.Y.: A neural network application for reliability modelling and condition-based predictive maintenance. Int. J. Adv. Manuf. Technol. 25(1–2), 174–179 (2005)
Bey-Temsamani, A., Engels, M., Motten, A., Vandenplas, S., Ompusunggu, A.P.: A practical approach to combine data mining and prognostics for improved predictive maintenance. Data Min. Case Stud. 36 (2009)
Wang, K.: Applying data mining to manufacturing: the nature and implications. J. Intell. Manuf. 18(4), 487–495 (2007)
Kohonen, T.: Self-Organizing Maps, vol. 30. Springer, Heidelberg (2012)
Díaz, I., Domínguez, M., Cuadrado, A.A., Fuertes, J.J.: A new approach to exploratory analysis of system dynamics using SOM. Applications to industrial processes. Expert Syst. Appl. 34(4), 2953–2965 (2008)
Romanowski, C.J., Nagi, R.: Analyzing maintenance data using data mining methods. In: Braha, D. (ed.) Data Mining for Design and Manufacturing. MACO, vol. 3, pp. 235–254. Springer, Boston (2001). https://doi.org/10.1007/978-1-4757-4911-3_10
Susto, G.A., Schirru, A., Pampuri, S., McLoone, S., Beghi, A.: Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans. Industr. Inf. 11(3), 812–820 (2015)
Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., Zeileis, A.: Conditional variable importance for random forests. BMC Bioinform. 9(1), 307 (2008)
Groggert, S., Wenking, M., Schmitt, R.H., Friedli, T.: Status quo and future potential of manufacturing data analytics—an empirical study. In: 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 779–783. IEEE (2017)
Acknowledgments
This work was carried out within the UPTIME project. UPTIME project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 768634.
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
Pertselakis, M., Lampathaki, F., Petrali, P. (2019). Predictive Maintenance in a Digital Factory Shop-Floor: Data Mining on Historical and Operational Data Coming from Manufacturers’ Information Systems. In: Proper, H., Stirna, J. (eds) Advanced Information Systems Engineering Workshops. CAiSE 2019. Lecture Notes in Business Information Processing, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-20948-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-20948-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20947-6
Online ISBN: 978-3-030-20948-3
eBook Packages: Computer ScienceComputer Science (R0)