Anna-Christina Glock, Johannes Fürnkranz,
"Dynamic Time Warping for Phase Recognition in Tribological Sensor Data"
, in Robert Wrembel and Silvia Chiusano and Gabriele Kotsis and Tjoa, A Min and Ismail Khalil: Proceedings of the 26th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2024), Serie Lecture Notes in Computer Science, Nummer 14912, Springer-Verlag, Naples, Italy, Seite(n) 245--250, 2024
Original Titel:
Dynamic Time Warping for Phase Recognition in Tribological Sensor Data
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 26th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2024)
Original Kurzfassung:
This paper analyzes the potential of dynamic time warping (DTW) for recognizing phases of tribological sensor data. The three classes in these time series?run-in, constant wear, and divergent wear?are distinguished by their long-term trend and curvature. A set of reference data for each class is needed for the classification. Each time series in the reference set represents a typical shape of this class. The classification is done by computing the DTW between a given time series and each reference time series, and assigning it to the class with the minimum distance. In experiments on simulated and real-world time series, we show that DTW is capable of correctly classifying whole time series representing one class. Additional experiments are done to analyze the capability of DTW to classify a time series that is only a part of the entire time series representing one class. During these experiments, limitations arose that demonstrated the importance of the choice of good reference data.
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