Abstract
This study presents our ongoing research on designing new methods for changepoint detection in industrial environments using a CUSUM method variant. The changepoint detection refers to identifying the location of change of some aspect in a given time series. The significant difference concerning a state-of-the-art time series prediction technique (using an LSTM) is that our method can handle anomalies masked by non-trivial trends. We have evaluated our proposal with a systematic series of test data and an example set with wear-induced anomalies.
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Acknowledgements
We thank the anonymous reviewers for theil helpful comments to improve the manuscript. This work has been supported by the project AutoDetect (Project No. 862019; Innovative Upper Austria 2020 (call Digitalization)) as well as the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the Province of Upper Austria in the frame of the COMET center SCCH.
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Mahmoud, S., Martinez-Gil, J., Praher, P., Freudenthaler, B., Girkinger, A. (2021). Deep Learning Rule for Efficient Changepoint Detection in the Presence of Non-Linear Trends. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2021 Workshops. DEXA 2021. Communications in Computer and Information Science, vol 1479. Springer, Cham. https://doi.org/10.1007/978-3-030-87101-7_18
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DOI: https://doi.org/10.1007/978-3-030-87101-7_18
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