Semi-Supervised Transfer Graph Representation Learning with Few-Shot Adaptation for gearbox diagnostics under extraneous transient noise

Gearboxes are critical mechanical components widely deployed in industrial applications, where their reliable operation directly impacts system safety and efficiency. However, conventional fault diagnostic approaches face significant challenges when operating under extraneous transient noise conditions, particularly with limited labeled fault samples. These challenges manifest as performance degradation with extremely sparse labeled datasets, vulnerability in pseudo-label generation mechanisms under intense transient noise, and inconsistent feature scale representations due to noise-induced interference. Furthermore, existing methods struggle to maintain diagnostic accuracy when confronted with both data…

• NN
  National Natural Science Foundation of China

  Awards: U23B20104, 52305085, 52405009, 52375078, 52105111
• CP
  China Postdoctoral Science Foundation

  Award: 2023M740021
• NS
  Natural Science Foundation of Anhui Province

  Award: 2108085QE229
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Award: 2025A1515012256