A novel multi-sensor local and global feature fusion architecture based on multi-sensor sparse Transformer for intelligent fault diagnosis

Abstract

Deep learning has been widely used for intelligent fault diagnosis of rotating machinery. However, owing to the limitations of training sample data and the complex industrial environments with variable operating conditions and noise interference, the existing deep learning-based fault diagnosis methods have difficulty achieving satisfactory performance. To address these issues, this paper proposes a novel multi-sensor local and global feature fusion architecture for intelligent fault diagnosis. First, through the integration of a stem structure and one-dimensional convolutions, a local feature perception mechanism is constructed to learn the sensor-specific local features within each sensor. Second, a global feature perception mechanism, which incorporates a multi-sensor sparse Transformer and a hierarchical architecture, is established to fully explore the sensor-specific global features within each sensor and the cross-sensor global features among multiple sensors. Third, the sensor-specific and cross-sensor global features are fed into a feature aggregation module to obtain the final multi-sensor global features. Finally, these multi-sensor global features are classified through a multi-sensor feature classifier to obtain diagnostic results. The experimental results obtained for a gear case and an inter-shaft bearing case demonstrate the superior diagnostic performance of the proposed method compared to the state-of-the-art comparative methods under limited training samples and complex environments.