Particle Filtering for Diagnosis and Prognosis of Anomalies In Rocket Engine Tests
Computing Sciences and Computer Engineering
We present a novel technique for anomaly detection and prognosis in sensor data from rocket engine test stands. We apply a combination of particle filtering and machine learning approaches to capture the model of nominal operations, and use voting techniques in conjunction with particle filtering to detect anomalies in test runs. We use two approaches — pure particle filtering and pure machine learning — for prognosis. Our experiments on test stand sensor data show successful detection of a known anomaly in the test data, while producing almost no false positives. Both prognostic approaches, however, predict no further impact had the test been completed, perhaps indicating that the anomaly was innocuous.
(2011). Particle Filtering for Diagnosis and Prognosis of Anomalies In Rocket Engine Tests. Infotech@Aerospace 2011.
Available at: https://aquila.usm.edu/fac_pubs/19355