DATA ANALYSIS PROCESS OF WORKING HYDRAULICS OF SMALL MOBILE MACHINE
Keywords:
mobile machine, data analysis, condition monitoring, hydraulics, forkliftAbstract
Changing work sequences and the operational environment makes the condition monitoring of mobile work machines more challenging compared with industrial systems. This sets special demands in regard to the analysis of the data measured from the machine during operation. A forklift, reach stacker, is used here as a research platform to study the operation of the data analysis process of working hydraulics of small mobile machine. The focus in the data analysis process is on feature extraction and classification parts. Discrete wavelet analysis is used to extract features which are then classified using the Self-Organizing Map (SOM). In addition, the sensitivity of data analysis process is studied. A simulation model of the lifting movement of the forklift is made to study the effects of changes in the fault levels of the performance of the data analysis methods.
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References
Ahola, J., Alhoniemi, E. and Simula, O. 1999. Monitoring
Industrial Processed Using the Self-
Organizing Maps. Proceedings of the 1999 IEEE
Midnight-Sun Workshop on Soft Computing Methods
in Industrial Applications, Kuusamo, Finland,
June, 1999. pp. 22 - 27.
Alhoniemi, E., Hollmén, J., Simula, O. and Vesanto,
J. 1999. Process Monitoring and Modeling Using
the Self-Organizing Map. Integrated Computer-
Aided Engineering, Vol. 6, No. 1, pp. 3 - 14.
Daubechies, I. 1990. The Wavelet Transform, Time-
Frequency Localization and Signal Analysis. IEEE
Transactions on Information Theory, Vol. 36,
No. 5, pp. 961 - 1005.
Duda, R., Hart, P. and Stork, D. 2001. Pattern Classification.
A Wiley-Interscience Publication, New
York, USA, 2nd edition.
Hiirsalmi, M. 2003. On Methods for Online Detection
of Disturbances in Process Data. Espoo, Finland,
VTT Research Report TTE1-2003-25. 28 p.
Karray, F. and de Silva, C. 2004. Soft Computing and
Intelligent Systems Design. Pearson Education, UK,
st edition.
Kasslin, M., Kangas, J. and Simula, O. 1992. Process
State Monitoring Using Self-Organizing Maps. In:
Aleksander, I. and Taylor, J. (ed). Artificial Neural
Networks 2: Proceedings of the 1992 International
Conference, ICAN’92. Vol. 2. Amsterdam, Netherlands,
Elsevier. September, 1992. pp. 1531 - 1534.
Kohonen, T. 2001. Self-Organizing Maps. Springer-
Verlag Berlin Heidelberg, New York, USA, 3rd edition.
Krogerus, T., Vilenius, J., Liimatainen, J. and
Koskinen, K. T. 2006. Self-Organizing Maps with
Unsupervised Learning for Condition Monitoring of
Fluid Power Systems. Fluid Power for Mobile, In-
Plant, Field and Manufacturing, SAE SP-2054,
Rosemont, Illinois, USA, October 31 - November 2,
pp. 43 - 51.
Krogerus, T., Sairiala, H., Saarinen, M. and Koskinen,
K. T. 2007. Fault Classification Based on Self-
Organizing Maps in Water Hydraulic Forklift. Proceedings
of the Tenth Scandinavian International
Conference on Fluid Power, SICFP’07, Tampere,
Finland, May 21 - 23, 2007. pp. 61 - 76.
Krogerus, T., Sairiala, H. and Koskinen, K. T.
a. Water Hydraulic Forklift with Intelligent
Condition Monitoring System. Proceedings of the
st National Conference on Fluid Power, Las Vegas,
Nevada, USA, March 12-14, 2008. pp. 337 -
Krogerus, T., Pietikäinen, J. and Koskinen, K. T.
b. Comparison of Vibration and Pressure Signals
for Fault Detection on Water Hydraulic Proportional
Valve. Proceedings of the 7th JFPS International
Symposium on Fluid Power, Toyama, Japan,
September 15-18, 2008. pp. 691 - 696.
Krogerus, T. 2011. Feature Extraction and Self-
Organizing Maps in Condition Monitoring of Hydraulic
Systems. Dissertation. Tampere, Finland.
Tampere University of Technology. Publication
126 p.
Mallat, S. 1989. A Theory for Multiresolution Signal
Decomposition: the Wavelet Representation. IEEE
Transactions on Pattern Analysis and Machine Intelligence,
Vol. 11, No. 7, 674 - 693.
Mallat, S. 1999. A Wavelet Tour of Signal Processing,
Academic Press, London, UK, 2nd edition, 1999.
Mathworks Inc. 2010. www.mathworks.com [Referred
6.2010].
Ramdén, T. 1998. Condition Monitoring and Fault
Diagnosis of Fluid Power Systems - A Approaches
with Neural Networks and Parameter Identification.
Dissertation. Linköping, Sweden. Linköping University.
No. 514. 195 p.
SOM Toolbox. 2010. www.cis.hut.fi/projects/somtoolbox
[Referred 27.6.2010].
Vesanto, J., Himberg, J., Alhoniemi, E. and
Parhankangas, J. 2000. SOM Toolbox for Matlab
Espoo, Finland, Technical Report on SOM Toolbox
0. 60 p.
Zachrison, A. 2008. Fluid Power Applications Using
Self-Organising Maps in Condition Monitoring.
Dissertation. Linköping, Sweden. Linköping University.
No. 1163. 56 p.