<p>Under the dual influence of time-varying working conditions and noise interference, accurately faults diagnosing in mechanical equipment poses significant challenges. Therefore, this paper proposes a limited sample fault diagnosis method using dilation kernel gated recurrent dropout attention unit (GRDAU) for time-varying speed based on interference suppression. Firstly, dilation kernel parameters were integrated into traditional convolution to suppress high-frequency noise. Secondly, to enhance the model’s robustness against noise interference and variations in speed, a GRDAU was developed based on gated recurrent unit (GRU). Additionally, a global cyclic dynamic decay learning strategy was implemented within the GRDAU to better adapt to complex speed variation conditions. Finally, two case studies were conducted to validate the robustness and interference suppression capabilities of the GRDAU. When compared to a range of existing advanced diagnostic methods, it demonstrated superior performance and stronger generalization ability.</p>