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To meet the fault prediction needs of hydro-generator units in the context of big data, combining the characteristics of the attention mechanism with good feature extraction ability and the advantages of model robustness for multi-sensor information driving, this paper proposed a fault prediction system of hydro-generating units based on the attention mechanism and multi-sensor information driving. The system was applied to on-line monitoring of unit ^#8 in August for a hydropower station in Hunan Province. The actual operation results show that the system can effectively predict the vibration trend of the hydro-generator set and realize the intelligent prediction of the hydro-generator.

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为满足水轮发电机组在大数据背景下的故障预测需求，结合注意力机制特征提取能力强的特性和多传感器信息驱动可提高模型鲁棒性的优点，提出了一种基于注意力机制和多传感器信息驱动（Bi-GRU-Attention）模型的水轮发电机组故障预测系统，并将其应用于湖南省某水电站^#8机组8月在线监测数据中。实际运行结果表明，该系统有效预测了水轮发电机组的振动趋势，实现了水轮发电机的智能预测。

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谭刚（1998-），男，硕士研究生，研究方向为故障诊断，E-mail：947675637@qq.com

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谭刚（1998-），男，硕士研究生，研究方向为故障诊断，E-mail：947675637@qq.com

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