Leakage Fault Diagnosis of Wind Tunnel Valves Using Wavelet Packet Analysis and Vision Transformer-Based Deep Learning

Fan Yi, Ruoxi Zhong, Wenjie Zhu, Run Zhou, Ying Wang and Li Guo ()
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Fan Yi: High Speed Aerodynamics Institute of China Aerodynamics Research and Development Center, Mianyang 621000, China
Ruoxi Zhong: High Speed Aerodynamics Institute of China Aerodynamics Research and Development Center, Mianyang 621000, China
Wenjie Zhu: High Speed Aerodynamics Institute of China Aerodynamics Research and Development Center, Mianyang 621000, China
Run Zhou: High Speed Aerodynamics Institute of China Aerodynamics Research and Development Center, Mianyang 621000, China
Ying Wang: Jiangsu Key Laboratory of Mechanical Analysis for Infrastructure and Advanced Equipment, School of Civil Engineering, Southeast University, Nanjing 210096, China
Li Guo: Jiangsu Key Laboratory of Mechanical Analysis for Infrastructure and Advanced Equipment, School of Civil Engineering, Southeast University, Nanjing 210096, China

Mathematics, 2025, vol. 13, issue 19, 1-0

Abstract: High-frequency vibrations in annular gap type pressure-regulating valves of wind tunnels can induce fatigue, fracture, and operational failures, posing challenges to safe and reliable operation. This study proposes a hybrid leakage fault diagnosis framework that integrates wavelet packet-based signal analysis with advanced deep learning techniques. Time-domain acceleration signals collected from multiple sensors are processed to extract maximum component energy and its variation rate, identified as sensitive and robust indicators for leakage detection. A fluid–solid coupled finite element model of the valve system further validates the reliability of these indicators under different operational scenarios. Based on this foundation, a Vision Transformer (ViT)-based model is trained on a dedicated database encompassing multiple leakage conditions and sensor arrangements. Comparative evaluation demonstrates that the ViT model outperforms conventional deep learning architectures in terms of accuracy, stability, and predictive reliability. The integrated framework enables fast, automated, and robust leakage diagnosis, providing a comprehensive solution to enhance the monitoring, maintenance, and operational safety of wind tunnel valve systems.

Keywords: pressure-regulating valve; leakage fault diagnosis; wavelet packet analysis; energy variation rate; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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