MPNet: A lightweight fault diagnosis network for rotating machinery

Abstract

Rotating machinery is prone to faults, especially bearing faults. Existing machinery fault diagnosis methods suffer from low accuracy and poor robustness under actual complex working conditions. To address these problems, this paper proposes a sound signal-based rotating machinery fault diagnosis method and designs a lightweight fault diagnosis network called MPNet. A multi-branch feature fusion module is constructed to capture the multi-scale correlation information of the sound features. A residual attention pyramid module is designed to adaptively learn abstract fault information at different levels, then the feature-enhanced attention maps at multi-scale generated by hierarchical fusion. Experimental results on a public bearing dataset and a self-made idler dataset reveal that the proposed method achieves 95.83% and 94.81% accuracy, respectively, meeting the requirements of lightweight and real-time detection. Compared with conventional methods, the proposed method has better diagnostic precision and robustness under strong noise. The code library is available at: https://github.com/xgli411/MPNet.