Particle Filtering for Diagnosis and Prognosis of Anomalies in Rocket Engine Tests

Abstract

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. We present the application of two well-known AI techniques ‐ Bayesian filtering and machine learning ‐ to the problems of diagnosis and prognosis of anomalies in sensor network data. Our objective was to develop a system that post-processes a csv file showing the sensor readings and activities (time-series) from a rocket engine test, and detect any anomalies that might have occurred during the test, as well as predict the future evolution of these (and other) anomalies if the test was allowed to continue. The output was required to be in the form of the names of the sensors that show anomalous behavior, and the start and end time of each anomaly, both diagnosed and predicted. Since our approach was model-based, we needed to automatically learn a model of nominal behavior from tests that were marked nominal. In this paper, we will describe this system and show experimental results that demonstrate that it has successfully detected a known anomaly in a given test stand data set, and delivered a prognosis that matches the broad conclusion of the test engineers. The paper is organized as follows. In section III we present the theoretical background, viz., dynamic Bayesian networks and the particle filtering framework that underlies our approach. In section IV we present our anomaly detection model and explain how it is tied to the particle filtering framework. In section V we describe anomaly detection module in detail, and present the experimental results in section V.D. In section VI we present the prognosis module and mention the experimental results. We present our conclusions and future work in section VII.