Unsupervised Anomaly Detection and Diagnosis for Liquid Rocket Engine Propulsion

@article{Martin2007UnsupervisedAD,
  title={Unsupervised Anomaly Detection and Diagnosis for Liquid Rocket Engine Propulsion},
  author={R. Martin},
  journal={2007 IEEE Aerospace Conference},
  year={2007},
  pages={1-15}
}
  • R. Martin
  • Published 2007 in 2007 IEEE Aerospace Conference
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… CONTINUE READING