Unsupervised Anomaly Detection and Diagnosis for Liquid Rocket Engine Propulsion

The results of a comprehensive array of unsupervised anomaly detection algorithms applied to Space Shuttle main engine (SSME) data are presented. Most of the algorithms are based upon variants of the well-known unconditional Gaussian mixture model (GMM). One goal of the paper is to demonstrate the maximum utility of these algorithms by the exhaustive development of a very simple GMM. Selected variants will provide us with the added benefit of diagnostic capability. Another algorithm that shares a common technique for detection with the GMM is presented, but instead uses a different modeling paradigm. The model provides a more rich description of the dynamics of the data, however the data requirements are quite modest. We will show that this very simple and straightforward method finds an event that characterizes a departure from nominal operation. We show that further diagnostic investigation with the GMM-based method can be used as a means to gain insight into operational idiosyncrasies for this nominally categorized test. Therefore, by using both modeling paradigms we can corroborate planned operational commands or provide warnings for unexpected operational commands.