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
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http://dx.doi.org/10.3384/ecp1392a37Ingår i: 13th Scandinavian International Conference on Fluid Power; June 3-5; 2013; Linköping; Sweden
Linköping Electronic Conference Proceedings 92:37, s. 379-388
Publicerad: 2013-09-09
ISBN: 978-91-7519-572-8
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
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
Mobile machine; analysis; diagnostics; operating state; hydraulics; time series; segmentation; episode; event; piecewise linear regression; clustering; classification; association rules; quantization error
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