Complementary-Integrated Approach to Model-Based and Data-Driven Prognostics and Fault Diagnosis for Reusable Rocket Engine Systems

The cost reduction of engine maintenance is an important issue for a future reusable launch vehicle. Toward reliable operation of a reusable rocket, a novel health monitoring method using the phase plane trajectory of feature extracted sensor data by principal component analysis (PCA) is verified. Because there are few failure data to estimate new method, a simple physical model is constructed to produce two kinds of failure data duct trouble and reservoir leakage, and examine the effectiveness of this new health monitoring method. Results indicate that presented phase plane trajectory method has good visibility to make the change clear and make it easy to detect and identify failures. This new approach will contribute to the improvement of health monitoring technologies based on sensor data not only for the reusable rocket engine systems but also for general systems.