Anomaly Detection in Spectral Data Streams: The Degassing Cycle Scenarios

Monitoring the elemental composition of materials is of great importance in many scientific and industrial applications. Optical Emission Spectroscopy (OES) is a technique that enables this through the analysis of electromagnetic radiation emitted by a material at different wavelengths, producing spectral data streams. Unlike previous studies on unsupervised anomaly detection in spectral data streams, which mainly focused on air leaks under constant pressure conditions in a vacuum chamber, we collected simulated real-world data within the BV5 Benchmark. This benchmark involves degassing cycles with argon leaks, air leaks, or both. The main objective in BV5 is to distinguish abnormal from normal degassing cycles in real time. However, following previous benchmark settings, we also collected simulated real-world datasets for argon leaks under constant pressure conditions and for temporal anomaly scenarios involving either argon or air leaks. We then provide a progressive evaluation of online anomaly detectors, using only spectral data, across benchmarks. Our results show that OBKNN (TNone), IFASD, OIF, and KitNet consistently outperform competing approaches in spectral anomaly detection. Furthermore, KitNet and OBKNN (TZNorm) exhibit a progressive decline in performance compared to the original benchmark. Lastly, a comparative analysis of methods in degassing cycle scenarios highlights that spectral data can be combined with additional features to enhance anomaly detection performance. To this end, we utilized the collected spectral data and additional features in BV5 to evaluate state-of-the-art online anomaly detection methods. We introduce the Subset Framework (SF), which complements spectral data with an additional feature. We show that incorporating data related to vacuum conditions using SF, such as the electrical current from the OES plasma, improves the detection performance of many online methods. Experiments demonstrate that SF improves the effectiveness of the majority of online detectors, notably SFOBKNN (TNone), SFOBKNN (TZNorm), and SFIFASD, with increases of 0.384, 0.323, and 0.217 in VUS-PR, respectively.