Enhanced cross-domain signal and physics-based interpretation for fault diagnosis of aircraft brake control valve under limited onboard signal acquisition

Sensor data collection from commercial aircraft faces challenges such as incomplete datasets, difficulty in assessing sensor significance, and inability to detect anomalous time points, leading to issues like ambiguous brake control valve faults categories. To address these, a new modeling framework is proposed to improve fault mode distinguishability through high-dimensional mapping. This framework uses Variational Autoencoders for training, combining reconstruction error and latent space similarity. It trains low-dimensional sensor data in two rounds, gradually approximating the target domain and synthesizing high-dimensional samples, enhancing cross-domain feature representation. Additionally, a time-adaptive weight allocation mechanism in a Bidirectional Long Short-Term Memory highlights critical signals, while a multi-head spatial attention mechanism reduces irrelevant signals. Experimental results show that the proposed fault diagnosis approach for brake control valves, utilizing aircraft onboard sensor data, achieves over 96 % in accuracy, precision, recall, and F1-score, outperforming the best performance of six classical network models by approximately 5 %.