Prognostics CoE Publications

The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are neces- sary to quickly identify when a fault occurs, so that recov- ery actions can be taken or an abort procedure can be initi- ated. Model-based diagnosis solutions, established using an in-depth analysis and understanding of the underlying physi- cal processes, offer the advanced capability to quickly detect and isolate faults, identify their severity, and predict their ef- fects on system performance. We develop a physics-based model of a cryogenic propellant loading system, which de- scribes the complex dynamics of liquid hydrogen filling from a storage tank to an external vehicle tank, as well as the in- fluence of different faults on this process. The model takes into account the main physical processes such as highly non- equilibrium condensation and evaporation of the hydrogen vapor, pressurization, and also the dynamics of liquid hydro- gen and vapor flows inside the system in the presence of he- lium gas. Since the model incorporates multiple faults in the system, it provides a suitable framework for model-based di- agnostics and prognostics algorithms. Using this model, we analyze the effects of faults on the system, derive symbolic fault signatures for the purposes of fault isolation, and per- form fault identification using a particle filter approach. We demonstrate the detection, isolation, and identification of a number of faults using simulation-based experiments.