Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911595730305024 |
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| author | Krasnikov, Sergej Meitz, Lukas Bagheri, Samineh Heider, Michael Schöler, Thorsten Hähner, Jörg |
| author_facet | Krasnikov, Sergej Meitz, Lukas Bagheri, Samineh Heider, Michael Schöler, Thorsten Hähner, Jörg |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13928 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study Krasnikov, Sergej Meitz, Lukas Bagheri, Samineh Heider, Michael Schöler, Thorsten Hähner, Jörg Machine Learning 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. |
| title | Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.13928 |