Application of Wasserstein distance in fault detection for liquid-propellant rocket engines

Health monitoring can effectively improve the reliability of liquid-propellant rocket engines. Aiming at the problem of fault detection in health monitoring for liquid-propellant rocket engines, a method based on Wasserstein distance was proposed and verified using ground hot test data of an LOX/LH2 rocket engine. The core idea was to use Wasserstein generative adversarial network to simulate the sample distribution of normal data, and used its discriminator to calculate the Wasserstein distance between the test sample and the simulated distribution, so as to achieve fault detection. The results show that the proposed method can overcome the difficulty of insufficient fault data, detect the faults in the steady-state process effectively without false alarm, and is sensitive to early anomalies. In the case of a small number of training samples, when the Wasserstein distance threshold is set to 3σ, the method is sensitive to early anomalies during the start-up transient. Starting fault can still be effectively detected with the threshold of 5σ, with a false alarming rate of 12.5%.