Cloud-Based Management of Machine Learning Generated Knowledge for Fleet Data Refinement
- 403 Downloads
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.
KeywordsDistributed Knowledge Management Mobile machinery Cloud services Data preprocessing Machine learning
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.
- 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
- 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
- 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
- 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.2011.11.011CrossRefGoogle 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