A GCU-SAM Enhanced Transformer for Fault Diagnosis of Rotating Machinery

Abstract

Fault signals in rotating machinery typically manifest as long time-series data embedded with local high-frequency impulses. Traditional deep learning methods often struggle to simultaneously capture these transient local impacts and model long-term global degradation features. To address this challenge, this paper proposes a novel GCU-SAM enhanced Transformer for intelligent fault diagnosis. The proposed network integrates a Gated Convolutional Unit (GCU) with a Self-Attention Mechanism (SAM). By introducing the GCU as a local inductive bias prior to the global attention module, the model dynamically captures and purifies local impulse responses via reset and update gates. Subsequently, a cascaded multi-head self-attention mechanism models long-sequence global evolution trends, forming an integrated framework for local fine-grained perception and global correlation modeling. Validated on the CWRU bearing and SEU gearbox datasets, the proposed architecture achieves superior diagnostic performance with an average F1-score of 99.01%. Compared to representative baselines, including 1D-CNN, BiLSTM, and the standard Transformer, the GCU-SAM significantly boosts diagnostic accuracy and effectively overcomes the early-stage optimization oscillations inherent in pure attention mechanisms. By achieving a deep multi-scale fusion of local abrupt changes and global degradation trends, the model exhibits exceptional feature-clustering discriminative capability and convergence stability, providing a robust and highly accurate solution for the intelligent fault diagnosis of complex rotating machinery.