Predictive Maintenance in a Digital Factory Shop-Floor: Data Mining on Historical and Operational Data Coming from Manufacturers’ Information Systems

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 349)


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.


Predictive maintenance Data mining Manufacturing Knowledge discovery Machine learning Digital factory 



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.


  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Wang, K.: Applying data mining to manufacturing: the nature and implications. J. Intell. Manuf. 18(4), 487–495 (2007)CrossRefGoogle Scholar
  7. 7.
    Kohonen, T.: Self-Organizing Maps, vol. 30. Springer, Heidelberg (2012)zbMATHGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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). Scholar
  10. 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)CrossRefGoogle Scholar
  11. 11.
    Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., Zeileis, A.: Conditional variable importance for random forests. BMC Bioinform. 9(1), 307 (2008)CrossRefGoogle Scholar
  12. 12.
    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)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Suite5 Data Intelligence Solutions LimitedLimassolCyprus
  2. 2.Whirlpool EMEABenton HarborUSA

Personalised recommendations