Fault Diagnosis of Core Drilling Rig Gearbox Based on Transformer and DCA-xLSTM

The gearbox is a core component of drilling rigs, valued for its high efficiency and load capacity. However, prolonged operation under heavy loads makes it prone to wear and failure. Complicating diagnosis, the vibration signals generated are highly complex and nonlinear. To achieve accurate fault diagnosis under varying operating conditions, we propose a novel method named T-DCAx, which integrates a Dual-path Convolutional Attention network, an extended Long Short-Term Memory network (xLSTM), and a Transformer. Our model leverages the complementary strengths of these components: the xLSTM module, enhanced with exponential gating and a novel memory mechanism, excels at modeling long-term temporal dependencies and mitigating gradient vanishing issues. The Transformer module effectively captures global contextual information through self-attention. These are synergized with a dual-path convolutional attention structure to ensure effective joint learning of both local–temporal and global patterns. Finally, a dedicated gearbox test platform was established to collect vibration signals under various conditions and fault types. The proposed T-DCAx method was validated on this dataset and demonstrated superior performance against several benchmark methods in comparative analyses.