A Multi-Sensor Fault Diagnosis Method for Aero-Engine Bearings Based on Complex-Valued Convolution and Dual Attention Mechanism

Abstract

Aero engines are widely used in modern aviation due to their high thrust-to-weight ratio, high efficiency, and high reliability, placing greater demands on the operational safety of key components such as bearings. Traditional bearing fault diagnosis methods typically rely on vibration signals collected by a single sensor, which makes it difficult to handle challenges such as incomplete information and noise interference in industrial settings. The paper proposes an intelligent fault diagnosis model called the Time-Frequency Attention Network, which is based on a time-frequency-aware convolutional layer and a fused attention mechanism. The goal is to fully exploit the time-frequency feature information from multi-sensor signals. First, a time-frequency-aware convolutional layer is designed using a kernel function constrained by the Short-Time Fourier Transform, leveraging a complex-valued convolution structure to effectively extract non-stationary features and local instantaneous frequency variations. Subsequently, a fused attention module is constructed, introducing a dual-attention mechanism in both channel and spatial dimensions to adaptively adjust the response intensity and frequency-domain focus areas of different sensor signals. The proposed network is experimentally validated on the Harbin Institute of Technology bearing dataset, achieving an accuracy of 99.54%. The results demonstrate that the proposed method outperforms existing benchmark models in terms of fault recognition accuracy and robustness, showcasing excellent diagnostic performance and generalization ability.