Enabling Centralised Management of Local Sensor Data Refinement in Machine Fleets

Petri Kannisto, David Hästbacka

2016

Abstract

In modern mobile machines, a lot of measurement data is available to generate information about machine performance. Exploiting it locally in machines would enable optimising their operation and, thus, yield competitive advantage and reduce environmental load due to reduced emissions. However, optimisation requires extensive knowledge about machine performance and characteristics in various conditions. As physical machines may be located geographically far from each other, the management of ever evolving knowledge is challenging. This study introduces a software concept to enable centralised management of data refinement performed locally in the machines of a geographically distributed fleet. It facilitates data utilisation in end user applications that provide useful information for operators in the field. Whatever the further data analysis requirements are, multiple preprocessing tasks are performed: it enables outlier limit configuration, the calculation of derived variables, data set categorisation and context recognition. A functional prototype has been implemented for the refinement of real operational data collected from forestry machines. The results show that the concept has considerable potential to bring added value for enterprises due to improved possibilities in managing data utilisation.

References

  1. Banerjee, T. P. and Das, S. (2012). Multi-sensor data fusion using support vector machine for motor fault detection. Information Sciences, 217:96 - 107.
  2. Basir, O. and Yuan, X. (2007). Engine fault diagnosis based on multi-sensor information fusion using dempstershafer evidence theory. Information Fusion, 8(4):379 - 386.
  3. Choudhury, T., Consolvo, S., Harrison, B., Hightower, J., Lamarca, A., Legrand, L., Rahimi, A., Rea, A., Bordello, G., Hemingway, B., Klasnja, P., Koscher, K., Landay, J., Lester, J., Wyatt, D., and Haehnel, D. (2008). The mobile sensing platform: An embedded activity recognition system. Pervasive Computing, IEEE, 7(2):32-41.
  4. Duan, L. and Xu, L. D. (2012). Business intelligence for enterprise systems: A survey. Industrial Informatics, IEEE Transactions on, 8(3):679-687.
  5. Favela, J., Tentori, M., Castro, L. A., Gonzalez, V. M., Moran, E. B., and Martínez-García, A. I. (2007). Activity recognition for context-aware hospital applications: Issues and opportunities for the deployment of pervasive networks. Mob. Netw. Appl., 12(2-3):155- 171.
  6. Fountas, S., Sorensen, C., Tsiropoulos, Z., Cavalaris, C., Liakos, V., and Gemtos, T. (2015). Farm machinery management information system. Computers and Electronics in Agriculture, 110:131-138.
  7. Golparvar-Fard, M., Heydarian, A., and Niebles, J. C. (2013). Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers. Advanced Engineering Informatics, 27(4):652 - 663.
  8. Hodge, V. and Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review , 22(2):85-126.
  9. Hou, L. and Bergmann, N. (2012). Novel industrial wireless sensor networks for machine condition monitoring and fault diagnosis. Instrumentation and Measurement, IEEE Transactions on, 61(10):2787-2798.
  10. Iftikhar, N. and Pedersen, T. B. (2011). Flexible exchange of farming device data. Computers and Electronics in Agriculture, 75(1):52 - 63.
  11. Jardine, A. K., Lin, D., and Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7):1483 - 1510.
  12. Kannisto, P., Hästbacka, D., Palmroth, L., and Kuikka, S. (2014). 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, pages 440-449.
  13. Kannisto, P., Hästbacka, D., Vilkko, M., and Kuikka, S. (2015). Service architecture and interface design for mobile machine parameter optimization system. IFAC-PapersOnLine, 48(3):848 - 854. 15th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2015.
  14. Khot, L. R., Tang, L., Blackmore, S., and Nørremark, M. (2006). Navigational context recognition for an autonomous robot in a simulated tree plantation. Transactions of the ASABE, 49(5):1579-1588.
  15. LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., and Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT sloan management review, 52(2):21-31.
  16. Lu, B. and Gungor, V. (2009). Online and remote motor energy monitoring and fault diagnostics using wireless sensor networks. Industrial Electronics, IEEE Transactions on, 56(11):4651-4659.
  17. Osborne, J. W. and Overbay, A. (2004). The power of outliers (and why researchers should always check for them). Practical assessment, research & evaluation, 9(6).
  18. Palmroth, L. (2011). Performance Monitoring and Operator Assistance Systems in Mobile Machines. PhD thesis, Department of Automation Science and Engineering, Tampere University of Technology, Tampere, Finland.
  19. Peets, S., Mouazen, A. M., Blackburn, K., Kuang, B., and Wiebensohn, J. (2012). Methods and procedures for automatic collection and management of data acquired from on-the-go sensors with application to onthe-go soil sensors. Computers and Electronics in Agriculture, 81:104 - 112.
  20. Steinberger, G., Rothmund, M., and Auernhammer, H. (2009). Mobile farm equipment as a data source in an agricultural service architecture. Computers and Electronics in Agriculture, 65(2):238 - 246.
  21. Stiefmeier, T., Roggen, D., Ogris, G., Lukowicz, P., and Tröster, G. (2008). Wearable activity tracking in car manufacturing. IEEE Pervasive Computing, 7(2):42- 50.
  22. Väyrynen, T., Peltokangas, S., Anttila, E., and Vilkko, M. (2015). Data-driven approach for analysis of performance indices in mobile work machines. In DATA ANALYTICS 2015, The Fourth International Conference on Data Analytics, pages 81-86.
  23. Yang, B.-S. and Kim, K. J. (2006). Application of dempstershafer theory in fault diagnosis of induction motors using vibration and current signals. Mechanical Systems and Signal Processing, 20(2):403 - 420.
Download


Paper Citation


in Harvard Style

Kannisto P. and Hästbacka D. (2016). Enabling Centralised Management of Local Sensor Data Refinement in Machine Fleets . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS, (IC3K 2016) ISBN 978-989-758-203-5, pages 21-30. DOI: 10.5220/0006045600210030


in Bibtex Style

@conference{kmis16,
author={Petri Kannisto and David Hästbacka},
title={Enabling Centralised Management of Local Sensor Data Refinement in Machine Fleets},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS, (IC3K 2016)},
year={2016},
pages={21-30},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006045600210030},
isbn={978-989-758-203-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS, (IC3K 2016)
TI - Enabling Centralised Management of Local Sensor Data Refinement in Machine Fleets
SN - 978-989-758-203-5
AU - Kannisto P.
AU - Hästbacka D.
PY - 2016
SP - 21
EP - 30
DO - 10.5220/0006045600210030