Abstract
Modern process and condition monitoring systems produce a huge amount of data which is hard to analyze manually. Previous analyzing techniques disregard time information and concentrate only for the indentification of normal and abnormal operational states. We present a new method for visualizing operational states and overall order of the transitions between them. This method is implemented to a visualization tool which helps the user to see the overall development of operational states allowing to find causes for abnormal behaviour. In the end visualization tool is tested in practice with real time series data collected from gear unit.
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Pylvänen, M., Äyrämö, S., Kärkkäinen, T. (2009). Visualizing Time Series State Changes with Prototype Based Clustering. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2009. Lecture Notes in Computer Science, vol 5495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04921-7_63
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DOI: https://doi.org/10.1007/978-3-642-04921-7_63
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