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Cloud-Based Management of Machine Learning Generated Knowledge for Fleet Data Refinement

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Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 914))

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Abstract

The modern mobile machinery has advanced on-board computer systems. They may execute various types of applications observing machine operation based on sensor data (such as feedback generators for more efficient operation). Measurement data utilisation requires preprocessing before use (e.g. outlier detection or dataset categorisation). As more and more data is collected from machine operation, better data preprocessing knowledge may be generated with data analyses. To enable the repeated deployment of that knowledge to machines in operation, information management must be considered; this is particularly challenging in geographically distributed fleets. This study considers both data refinement management and the refinement workflow required for data utilisation. The role of machine learning in data refinement knowledge generation is also considered. A functional cloud-managed data refinement component prototype has been implemented, and an experiment has been made with forestry data. The results indicate that the concept has considerable business potential.

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References

  1. Bahga, A., Madisetti, V.K.: Analyzing massive machine maintenance data in a computing cloud. IEEE Trans. Parallel Distrib. Syst. 23(10), 1831–1843 (2012). https://doi.org/10.1109/TPDS.2011.306

    Article  Google Scholar 

  2. Banerjee, T.P., Das, S.: Multi-sensor data fusion using support vector machine for motor fault detection. Inf. Sci. 217, 96–107 (2012). https://doi.org/10.1016/j.ins.2012.06.016

    Article  Google Scholar 

  3. Basir, O., Yuan, X.: Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory. Inf. Fusion 8(4), 379–386 (2007). https://doi.org/10.1016/j.inffus.2005.07.003

    Article  Google Scholar 

  4. Choudhury, T., et al.: The mobile sensing platform: an embedded activity recognition system. IEEE Pervasive Comput. 7(2), 32–41 (2008). https://doi.org/10.1109/MPRV.2008.39

    Article  Google Scholar 

  5. Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends Sig. Process. 7(34), 197–387 (2014). https://doi.org/10.1561/2000000039

    Article  MathSciNet  Google Scholar 

  6. Duan, L., Xu, L.D.: Business intelligence for enterprise systems: a survey. IEEE Trans. Ind. Inform. 8(3), 679–687 (2012). https://doi.org/10.1109/TII.2012.2188804

    Article  Google Scholar 

  7. Favela, J., et al.: Activity recognition for context-aware hospital applications: issues and opportunities for the deployment of pervasive networks. Mob. Netw. Appl. 12(2–3), 155–171 (2007). https://doi.org/10.1007/s11036-007-0013-5

    Article  Google Scholar 

  8. Filev, D., Lu, J., Hrovat, D.: Future mobility: integrating vehicle control with cloud computing. Mech. Eng. 135(3), S18–S24 (2013)

    Article  Google Scholar 

  9. Fountas, S., Sorensen, C., Tsiropoulos, Z., Cavalaris, C., Liakos, V., Gemtos, T.: Farm machinery management information system. Comput. Electron. Agric. 110, 131–138 (2015). https://doi.org/10.1016/j.compag.2014.11.011

    Article  Google Scholar 

  10. Golparvar-Fard, M., Heydarian, A., Niebles, J.C.: Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers. Adv. Eng. Inform. 27(4), 652–663 (2013). https://doi.org/10.1016/j.aei.2013.09.001

    Article  Google Scholar 

  11. Hodge, V., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004). https://doi.org/10.1007/s10462-004-4304-y

    Article  MATH  Google Scholar 

  12. Hou, L., Bergmann, N.: Novel industrial wireless sensor networks for machine condition monitoring and fault diagnosis. IEEE Trans. Instrum. Meas. 61(10), 2787–2798 (2012). https://doi.org/10.1109/TIM.2012.2200817

    Article  Google Scholar 

  13. Iftikhar, N., Pedersen, T.B.: Flexible exchange of farming device data. Comput. Electron. Agric. 75(1), 52–63 (2011). https://doi.org/10.1016/j.compag.2010.09.010

    Article  Google Scholar 

  14. Jardine, A.K., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Sig. Process. 20(7), 1483–1510 (2006). https://doi.org/10.1016/j.ymssp.2005.09.012

    Article  Google Scholar 

  15. Kannisto, P., Hästbacka, D.: Enabling centralised management of local sensor data refinement in machine fleets. In: Proceedings of the 8th International Conference on Knowledge Management and Information Sharing, vol. 3, pp. 21–30 (2016). https://doi.org/10.5220/0006045600210030

  16. Kannisto, P., Hästbacka, D., Kuikka, S.: System architecture for mastering machine parameter optimisation. Comput. Ind. 85, 39–47 (2017). https://doi.org/10.1016/j.compind.2016.12.006

    Article  Google Scholar 

  17. Kannisto, P., Hästbacka, D., Palmroth, L., Kuikka, S.: Distributed knowledge management architecture and rule based reasoning for mobile machine operator performance assessment. In: Proceedings of the 16th International Conference on Enterprise Information Systems, pp. 440–449 (2014). https://doi.org/10.5220/0004870004400449

  18. Khot, L.R., Tang, L., Blackmore, S., Nørremark, M.: Navigational context recognition for an autonomous robot in a simulated tree plantation. Trans. ASABE 49(5), 1579–1588 (2006)

    Article  Google Scholar 

  19. LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52(2), 21–31 (2011)

    Google Scholar 

  20. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  21. Lu, B., Gungor, V.: Online and remote motor energy monitoring and fault diagnostics using wireless sensor networks. IEEE Trans. Ind. Electron. 56(11), 4651–4659 (2009). https://doi.org/10.1109/TIE.2009.2028349

    Article  Google Scholar 

  22. March, S.T., Smith, G.F.: Design and natural science research on information technology. Decis. Support. Syst. 15(4), 251–266 (1995). https://doi.org/10.1016/0167-9236(94)00041-2

    Article  Google Scholar 

  23. Osborne, J.W., Overbay, A.: The power of outliers (and why researchers should always check for them). Pract. Assess. Res. Eval. 9(6), 1–12 (2004)

    Google Scholar 

  24. Palmroth, L.: Performance monitoring and operator assistance systems in mobile machines. Ph.D. thesis, Department of Automation Science and Engineering, Tampere University of Technology, Tampere, Finland (2011)

    Google Scholar 

  25. Peets, S., Mouazen, A.M., Blackburn, K., Kuang, B., Wiebensohn, J.: Methods and procedures for automatic collection and management of data acquired from on-the-go sensors with application to on-the-go soil sensors. Comput. Electron. Agric. 81, 104–112 (2012). https://doi.org/10.1016/j.compag.2011.11.011

    Article  Google Scholar 

  26. Steinberger, G., Rothmund, M., Auernhammer, H.: Mobile farm equipment as a data source in an agricultural service architecture. Comput. Electron. Agric. 65(2), 238–246 (2009). https://doi.org/10.1016/j.compag.2008.10.005

    Article  Google Scholar 

  27. Stiefmeier, T., Roggen, D., Ogris, G., Lukowicz, P., Tröster, G.: Wearable activity tracking in car manufacturing. IEEE Pervasive Comput. 7(2), 42–50 (2008). https://doi.org/10.1109/MPRV.2008.40

    Article  Google Scholar 

  28. Tao, F., Zhang, L., Liu, Y., Cheng, Y., Wang, L., Xu, X.: Manufacturing service management in cloud manufacturing: overview and future research directions. J. Manuf. Sci. Eng. 137(4), 040912 (2015). https://doi.org/10.1115/1.4030510

    Article  Google Scholar 

  29. Väyrynen, T., Peltokangas, S., Anttila, E., Vilkko, M.: Data-driven approach for analysis of performance indices in mobile work machines. In: Data Analytics 2015, The Fourth International Conference on Data Analytics, pp. 81–86 (2015)

    Google Scholar 

  30. Wan, J., Zhang, D., Zhao, S., Yang, L.T., Lloret, J.: Context-aware vehicular cyber-physical systems with cloud support: architecture, challenges, and solutions. IEEE Commun. Mag. 52(8), 106–113 (2014). https://doi.org/10.1109/MCOM.2014.6871677

    Article  Google Scholar 

  31. Whaiduzzaman, M., Sookhak, M., Gani, A., Buyya, R.: A survey on vehicular cloud computing. J. Netw. Comput. Appl. 40, 325–344 (2014). https://doi.org/10.1016/j.jnca.2013.08.004

    Article  Google Scholar 

  32. Wu, D., Rosen, D.W., Wang, L., Schaefer, D.: Cloud-based design and manufacturing: a new paradigm in digital manufacturing and design innovation. Comput.-Aided Des. 59, 1–14 (2015). https://doi.org/10.1016/j.cad.2014.07.006

    Article  Google Scholar 

  33. Yang, B.S., Kim, K.J.: Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mech. Syst. Signal Process. 20(2), 403–420 (2006). https://doi.org/10.1016/j.ymssp.2004.10.010

    Article  MathSciNet  Google Scholar 

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Acknowledgments

This work was made as a part of the D2I (Data to Intelligence) project funded by Tekes (the Finnish Funding Agency for Innovation). The authors would like to express their sincere gratitude to the project partners and participant companies.

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Authors and Affiliations

  1. Tampere University of Technology, P.O. Box 692, FI-33101, Tampere, Finland

    Petri Kannisto & David Hästbacka

Authors
  1. Petri Kannisto
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  2. David Hästbacka
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Corresponding author

Correspondence to Petri Kannisto .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

  2. Department of Software Technology, Delft University of Technology, Voorburg, Zuid-Holland, The Netherlands

    Jan Dietz

  3. Faculty of Exact Sciences and Engineering, University of Madeira, Funchal, Portugal

    David Aveiro

  4. Henley Business School, University of Reading, Reading, UK

    Kecheng Liu

  5. University of Coimbra, Coimbra, Portugal

    Jorge Bernardino

  6. Instituto Politecnico de SetĂşbal (IPS), SetĂşbal, Portugal

    Joaquim Filipe

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Kannisto, P., Hästbacka, D. (2019). Cloud-Based Management of Machine Learning Generated Knowledge for Fleet Data Refinement. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2016. Communications in Computer and Information Science, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-319-99701-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-99701-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99700-1

  • Online ISBN: 978-3-319-99701-8

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