Sensor Fault Diagnosis for Satellite Attitude Control System Based on WPE and OOA-BP Neural Network

Satellite attitude control system (SACS) is prone to faults due to its complexity and harsh working environment. If the fault is not detected in a timely manner to take appropriate countermeasures, it may lead to severe consequences. Traditional fault diagnosis methods have certain limitations, such as the need for accurate mathematical models or enough experience of fault diagnosis. Therefore, this paper proposes a fault diagnosis method for SACS sensors that integrates feature extraction with an optimized back propagation (BP) neural network. Firstly, the features of simulation data are extracted by wavelet packet energy (WPE), which can effectively highlight the fault characteristics. Then the osprey optimization algorithm (OOA) is used to optimize the initial weights and biases of BP neural network, so as to solve the problem that BP neural network is easy to fall into local minimum. The simulation results show that the proposed method can accurately detect and identify various fault types of sensors in SACS, thus providing a new and effective way for fault diagnosis of SACS.