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

• URL: http://arxiv.org/abs/2604.13928v1
• Date: Wed, 15 Apr 2026 14:35:20 GMT
• Title: Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study
• Abstract summary: anomaly detection methods validated under simplified conditions often fail to generalize to industrial settings.<n>This work presents an empirical study on a unique dataset collected from fully operational industrial machinery.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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