Industrial Time Series Anomaly Detection
Time series are ubiquitous in aerospace engineering and their processing can enable the generation of large business value. Manufacturing tools and more importantly, aerospace assets themselves produce large amounts of sensor signals, which cannot be analyzed and even captured in its totality by humans. In this talk we focus on automatic anomaly detection tasks for aircraft sensors. We assess the industrial viability of various semi-supervised anomaly detection systems based on Deep Learning for automatic discovery of point, contextual and collective anomalies on large datasets with little prior knowledge. Moreover, we present the results of a challenge with the same goals hosted by Airbus on its AIGym co-innovation platform, engaging over 150 academic and industrial teams worldwide.
Dr. Sergei Bobrovskyi is a Data Scientist within the Analytics Accelerator team of the Airbus Digital Transformation Office. His work focuses on applications of AI for anomaly detection in time series, spanning various use-cases across Airbus. Prior to Airbus he worked on automated fraud detection for one of the largest e-commerce companies in Germany. Before that he was engaged in various research related positions in the space industry.
Sergei holds a PhD in theoretical physics as well as a physics Diploma from the University of Hamburg. Besides physics he also studied philosophy with an emphasis on the philosophy of mind.