Conference article

Recognition of Operating States of a Medium-Sized Mobile Machine

Tomi Krogerus
Department of Intelligent Hydraulics and Automation, Tampere University of Technology, Finland

Mika Hyvönen
Department of Information and Computer Science, Aalto University School of Science, Espoo, Finland

K. Huhtala
Department of Information and Computer Science, Aalto University School of Science, Espoo, Finland

Kalevi Huhtala
Department of Intelligent Hydraulics and Automation, Tampere University of Technology, Finland

Download articlehttp://dx.doi.org/10.3384/ecp1392a37

Published in: 13th Scandinavian International Conference on Fluid Power; June 3-5; 2013; Linköping; Sweden

Linköping Electronic Conference Proceedings 92:37, p. 379-388

Show more +

Published: 2013-09-09

ISBN: 978-91-7519-572-8

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

In this study the main goal was to study the operating states of a medium-sized mobile machine. The measured time series data were analysed to find frequent episodes (sequences of operating states) to which the conditional probabilities were then calculated. The time series data were first segmented to find events. One or more segments build up an event which can be interpreted to be an operating state. The segments were then clustered and classified. The segment class labels were interpreted as events. As a result; a list of rules was established. The rules describe causal connections between consecutive operating states and transition probabilities from 1st state to 2nd state. The recognized operating states were further analysed to be used in diagnosis of the operation of the machine and focusing the diagnostics on certain operating states

Keywords

Mobile machine; analysis; diagnostics; operating state; hydraulics; time series; segmentation; episode; event; piecewise linear regression; clustering; classification; association rules; quantization error

References

[1] Uusisalo; J. 2011. A Case Study on Effects of Remote Control and Control System Distribution in Hydraulic Mobile Machines. Dissertation. Tampere; Finland. Tampere University of Technology. Publication 960. 111 p.

[2] Durrant-Whyte; H. 2005. Autonomous Land Vehicles. Proc. IMechE.; Part I: Journal of Systems and Control Engineering; 1; vol. 219; no. 1; pp. 77-98.

3] Krogerus; T. 2011. Feature Extraction and Self- Organizing Maps in Condition Monitoring of Hydraulic Systems. Dissertation. Tampere; Finland. Tampere
University of Technology. Publication 949. 126 p.

[4] Ruotsalainen; M.; Jylhä; J.; Vihonen; J. and Visa; A. 2009. A Novel Algorithm for Identifying Patterns from Multisensor Time Series. In Proceedings of then 2009 WRI World Congress on Computer Science and Information Engineering; CSIE 2009; Los Angeles; California; USA; 31 March - 2 April; 2009; vol. 5; pp. 100-105.

[5] Keogh; E.; Chu; S.; Hart; D. and Pazzani; M. 2011. An Online Algorithm for Segmenting Time Series. In Proceedings of IEEE International Conference on Data Mining; 2001; pp. 289-296.

[6] Mannila; H.; Toivonen; H. and Verkamo; I. 1997. Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery; vol. 1; no. 3; pp. 259-289.

[7] Backas; J.; Ahopelto; M.; Huova; M.; Vuohijoki; A.; Karhu; O.; Ghabcheloo; R. and Huhtala; K. 2011. IHAMachine:
A Future Mobile Machine. In Proceedings of The Twelfth Scandinavian International Conference on Fluid Power; SICFP’11; Tampere; Finland; May 18 20; 2011; Vol. 1; No. 4; pp. 161-176.

[8] Huova; M.; Karvonen; M.; Ahola; V.; Linjama; M. and Vilenius; M. 2010. Energy Efficient Control of Multiactuator Digital Hydraulic Mobile Machine. In Proceedings of 7th International Fluid Power Conference Aachen; Aachen; Germany; March 22 -24; 2010; vol. 1; 12 p.

[9] Theoridis; S. and Koutroumbas; K. 1999. Pattern Recognition; Academic Press; USA.

[10] Davies; D. and Bouldin; D. 1979. A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence; vol. 1; no. 2; pp. 224-227.

[11] Agrawal; R. and Srikant; R. 1994. Fast algorithms for nmining association rules. In Proceedings of the 20th VLDB conference; 1994; pp. 487–499.

[12] Ahola; J.; Alhoniemi; E. and Simula; O. 1999. Monitoring Industrial Processed Using the Self- Organizing Maps. In Proceedings of the 1999 IEEE Midnight-Sun Workshop on Soft Computing Methods in Industrial Applications; Kuusamo; Finland; June; 1999. pp. 22-27.

[13] 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.

Citations in Crossref