Compound fault diagnosis method for aircraft air data system based on UKF and capsule network

Abstract The air data system (ADS) consists of multiple sensors designed to measure atmospheric parameters during an aircraft’s flight. These measurements are then processed by the air data computer to compute the relevant flight parameters required for guidance and control. However, due to the system’s inherent complexity and the harsh flight environment, internal sensors are highly susceptible to failures, including scenarios where multiple components fail simultaneously. To address the issue of complex fault diagnosis in the aircraft air data system, this paper investigates a fault diagnosis method based on the unscented Kalman filter (UKF) and capsule network (CapsNet). First, a UKF model for atmospheric parameters is established based on the computational principles of the air data system, and parameters that can effectively reflect fault information are selected. Then, feature mode decomposition is applied to enhance fault characteristics, which are used as inputs for the neural network. Next, the convolutional layers of the CapsNet are improved using the InceptionV3 network to enhance feature extraction capabilities, and a multi-label classifier is designed to achieve fault location of compound faults. Finally, the proposed algorithm is validated on the simulation platform. Through ablation and comparative experiments, the proposed algorithm achieves a fault diagnosis accuracy of over 95%, demonstrating a more effective solution to the compound fault problem in the aircraft air data system. In our study, compared to convolutional neural networks (CNN), fully convolutional network (FCN), temporal convolutional network (TCN), Inception V3 and the original CapsNet, the improved CapsNet achieved the best performance in compound fault diagnosis.