Pressure chamber fault diagnosis model design based on segmented control and adaptive fuzzy neural network.

With the advent of the information age, the evolution of aerospace technology has rendered high-altitude flights increasingly common and vital. Nonetheless, the fault diagnosis of the pressure chamber, a crucial aspect of ensuring flight safety, remains an urgent challenge. The integration of segmented control technology in this domain further augments system stability and safety. This paper introduces a fault diagnosis model using EWTLM-FNN framework for monitoring and analyzing the state of the pressure chamber. The EWTLM-FNN framework commences with denoising and filtering of barometric pressure monitoring data to eliminate noise interference, followed by the extraction of frequency-domain modal information using the empirical wavelet transform (EWT). Subsequently, a three-layer Long Short-Term Memory Network conducts a profound analysis of the time and frequency domain features. The extracted features are then input into a fuzzy neural network (FNN) for fault identification and diagnosis, thus achieving high-precision monitoring of pressure chamber faults. Experimental results demonstrate that the proposed EWTLM-FNN framework exhibits superior fault diagnosis performance across multiple barometric pressure monitoring datasets, achieving over 90% diagnostic accuracy on the self-constructed pressure chamber fault dataset, and surpassing all indices compared to traditional machine learning and single deep learning models, thereby providing a theoretical and methodological foundation for future aircraft pressure fault diagnosis.