References

AI4I 2020 Predictive Maintenance Dataset. 2020. UCI Machine Learning Repository. https://doi.org/10.24432/C5HS5C.
Cuturi, Marco, and Mathieu Blondel. 2017. “Soft-Dtw: A Differentiable Loss Function for Time-Series.” International Conference on Machine Learning, 894–903.
Dogru, Oguzhan, Junyao Xie, Om Prakash, et al. 2024. “Reinforcement Learning in Process Industries: Review and Perspective.” IEEE/CAA Journal of Automatica Sinica 11 (2): 283–300. https://doi.org/10.1109/JAS.2024.124227.
Ehrensperger, Gregor. 2026a. “Industrial_datascience_live_machine: V1.0.0.” Zenodo, January. https://doi.org/10.5281/zenodo.18183373.
Ehrensperger, Gregor. 2026b. “Industrial_datascience_sim_machine_knifes: V1.0.2.” Zenodo, January. https://doi.org/10.5281/zenodo.18148106.
Faouzi, Johann. 2024. “Time Series Classification: A review of Algorithms and Implementations.” In Time Series Analysis - Recent Advances, New Perspectives and Applications, edited by Jorge Rocha, Sandra Oliveira, and Cláudia Viana. https://doi.org/10.5772/intechopen.1004810.
Holder, Christopher, and Anthony Bagnall. 2026. “Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering.” Data Mining and Knowledge Discovery 40 (2): 21. https://doi.org/10.1007/s10618-026-01189-9.
Holder, Christopher, Anthony Bagnall, and Jason Lines. 2024. “On Time Series Clustering with k-Means.” arXiv Preprint arXiv:2410.14269.
Klaeger, Tilman, Sebastian Gottschall, and Lukas Oehm. 2021. “Data Science on Industrial Data—Today’s Challenges in Brown Field Applications.” Challenges 12 (1). https://doi.org/10.3390/challe12010002.
Lahreche, Abdelmadjid, and Bachir Boucheham. 2021. “A Comparison Study of Dynamic Time Warping’s Variants for Time Series Classification.” International Journal of Informatics and Applied Mathematics 4 (1): 56–71. https://dergipark.org.tr/en/pub/ijiam/issue/59559/819482.
Mathur, Aeshita, Ameesha Dabas, and Nikhil Sharma. 2022. “Evolution from Industry 1.0 to Industry 5.0.” 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 1390–94. https://doi.org/10.1109/ICAC3N56670.2022.10074274.
Paparrizos, John, Fan Yang, and Haojun Li. 2024. “Bridging the Gap: A Decade Review of Time-Series Clustering Methods.” arXiv Preprint arXiv:2412.20582.
Peres, Ricardo Silva, Xiaodong Jia, Jay Lee, Keyi Sun, Armando Walter Colombo, and Jose Barata. 2020. “Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook.” IEEE Access 8: 220121–39. https://doi.org/10.1109/ACCESS.2020.3042874.
Roverso, Davide. 2002. “Plant Diagnostics by Transient Classification: The Aladdin Approach.” International Journal of Intelligent Systems 17 (8): 767–90.
Sutton, R. S., and A. G. Barto. 2018. Reinforcement Learning, Second Edition: An Introduction. Adaptive Computation and Machine Learning Series. MIT Press. https://books.google.at/books?id=sWV0DwAAQBAJ.
Tan, Chang Wei, Christoph Bergmeir, Francois Petitjean, and Geoffrey I Webb. 2020. “IEEEPPG Dataset.” Zenodo, June. https://doi.org/10.5281/zenodo.3902710.
Tan, Chang Wei, Christoph Bergmeir, François Petitjean, and Geoffrey I Webb. 2021. “Time Series Extrinsic Regression: Predicting Numeric Values from Time Series Data.” Data Mining and Knowledge Discovery 35 (3): 1032–60.
Wallsberger, Raphael, Ricardo Knauer, and Stephan Matzka. 2023. “Explainable Artificial Intelligence in Mechanical Engineering: A Synthetic Dataset for Comprehensive Failure Mode Analysis.” 2023 Fifth International Conference on Transdisciplinary AI (TransAI), 249–52. https://doi.org/10.1109/TransAI60598.2023.00032.
Zhao, Bendong, Huanzhang Lu, Shangfeng Chen, Junliang Liu, and Dongya Wu. 2017. “Convolutional Neural Networks for Time Series Classification.” Journal of Systems Engineering and Electronics 28 (1): 162–69. https://doi.org/10.21629/JSEE.2017.01.18.