Unsupervised Anomaly Detection in Multivariate Time Series Using Variational Autoencoders
In this master’s thesis, a novel unsupervised anomaly detection tool was developed in collaboration with Sandvik Rock Processing to assist engineers and experts in analyzing large amounts of sensor data from cone crushers used in the stone crushing industry. The tool focuses on analyzing power, pressure, and CSS sensor data. A crucial preprocessing step was implemented to algorithmically identify