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arXiv · 2604.13928

Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study

Sergej Krasnikov·Lukas Meitz·Samineh Bagheri·Michael Heider·Thorsten Schöler·Jörg Hähner
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Abstract

Industrial time-series data from real production environments exhibits substantially higher complexity than commonly used benchmark datasets, primarily due to heterogeneous, multi-stage operational processes. As a result, anomaly detection methods validated under simplified conditions often fail to generalize to industrial settings. This work presents an empirical study on a unique dataset collected from fully operational industrial machinery, explicitly capturing pronounced process-induced variability. We evaluate which model classes are capable of capturing this complexity, starting with a classical Isolation Forest baseline and extending to multiple autoencoder architectures. Experimental results show that Isolation Forest is insufficient for modeling the non-periodic, multi-scale dynamics present in the data, whereas autoencoders consistently perform better. Among them, temporal convolutional autoencoders achieve the most robust performance, while recurrent and variational variants require more careful tuning.

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Sergej Krasnikov, Lukas Meitz, Samineh Bagheri, Michael Heider, Thorsten Schöler, Jörg Hähner. 2026-04-15. Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study. https://arxiv.org/abs/2604.13928

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