Fault Detection and Diagnosis for Thrust Drop of Launch Vehicles Against Disturbances

Abstract

Rapid detection and accurate diagnosis for thrust drop fault are still open problems for launch vehicles. Existing studies have achieved fruitful results in fault detection and diagnosis (FDD) methods, leaving the complex disturbances not fully considered. The disturbances degrade FDD performance and are very hard to identify. In this paper, an FDD method based on multiple deep neural networks is proposed, which can deal with the complex disturbances. First, we split the FDD into two parts to reduce complexity. In detail, a fully connected neural network is selected to detect fault and diagnose fault degree, and a long short-term memory neural network is carefully tuned to diagnose fault mode. An analysis of the relationship between flight states and fault conditions is employed to design input of the fully connected neural network and the long short-term memory neural network. Then, a novel prediction and compensation method is proposed to deal with the unobservable and time-varying disturbances. Without extra information, data from the inertial measurement unit and navigation system are just enough to eliminate the disturbances, whereas existing methods may need more sensors. At last, illustrative simulations demonstrate the effectiveness and correctness of the proposed method.