A Dynamic Self-Attention-Based Fault Diagnosis Method for Belt Conveyor Idlers

Abstract

Idlers are typical rotating parts of a belt conveyor carrying the conveyor belt and materials. The complex operating noise and unstable features lead to poor accuracy of sound-based idler fault diagnosis. This paper proposes a fault diagnosis method for belt conveyor idlers based on Transformer’s dynamic self-attention (DSA). Firstly, the A-weighted time-frequency spectrum of the idler sound is extracted as the input. Secondly, based on the DSA block, the multi-frequency cross-correlation DSA algorithm is designed to extract the cross-correlation features between different frequency bands in the input feature map, and the global DSA algorithm is applied to perceive and enhance the global correlation features in parallel. Finally, the cross-correlation and global correlation features are concatenated and linearly projected into a fault-type space to diagnose typical bearing and roller faults of idlers. The method makes full use of the relevant information scattered in different frequency bands of the idler running sound under complex working conditions and reduces the negative effect of the strong running noise on the extraction of weak fault features. Experimental results show that the fault diagnosis accuracy is 94.6% and the latency is 27.8 ms.