To achieve early fault warning for feedwater pumps in power plants， a feedwater pump fault warning model， MHA-NGU， based on Multi-Head Attention （MHA） mechanism and New Gated Unit （NGU） was proposed. MHA mechanism was employed to capture long-range dependencies in the time series data， thereby enhancing key dimensions through multi-perspective feature interaction. NGU is an improved Recurrent Neural Network （RNN） proposed in this paper， in which the output gate and reset gate were designed with two levels， thereby avoiding the degradation in global dependency capture capability caused by single?level gates’ over?reliance on local information. Additionally， a T-transformation module was embedded within NGU to further alleviate the gradient vanishing problem of the model. Experimental validation was conducted using data from a 1 030 MW thermal power unit in Zhejiang Province as an example. The results demonstrate that the proposed model increases the Coefficient of Determination （R2） by 4.38% and 1.90% compared with GRU and the second-best model， MHA-GRU， respectively. Furthermore， it triggers a warning 5.39 hours earlier than the GRU model， realizing an early fault warning for feedwater pumps effectively.