Industrial systems generate large volumes of multivariate time series data that are complex, dynamic, and affected by noise, where early anomaly detection is critical to ensure operational safety and reliability of the system. Conventional machine learning methods often struggle with the non-linear behaviors, temporal dependencies, and subtle or latent faults that characterize real-world industrial environments. This paper proposes a hybrid anomaly detection framework that integrates Timed Automata to model the dynamic evolution of system behaviors with a Quantum Fidelity-based Fuzzy C-Means clustering algorithm to identify anomalous patterns. The proposed approach is validated on the Skoltech Anomaly Benchmark (SKAB), which simulates realistic industrial scenarios by injecting temporally localized anomalies that are sometimes masked by normal process fluctuations, making them particularly challenging to detect. The experiments provide a comparative analysis with state-of-the-art classical methods. The results highlight the potential of combining symbolic temporal modeling with quantum-inspired clustering to enhance anomaly detection, as demonstrated in an example of a complex, dynamic, and noisy industrial environment. • Hybrid anomaly detection framework integrating Timed Automata with Quantum Fidelity-based Fuzzy C-Means clustering. • Early detection of subtle and temporally localized anomalies in industrial multivariate time series. • Segmentation of time series into statistically homogeneous intervals to improve detection accuracy and interpretability. • Use of quantum fidelity metric enhances clustering expressiveness over traditional distance measures. • Validated on the Skoltech Anomaly Benchmark (SKAB), outperforming state-of-the-art classical methods.
Acampora et al. (Sun,) studied this question.