研究大型核电设备的故障预警方法，对于故障的及时排除、降低安全风险、减少非必要成本以及提高发电效率具有深远的意义。传统的核电设备预警，大多是实时监控报警系统，预警效果有待提升，也未充分利用数据价值。文中提出了一种基于贝叶斯BiLSTM的故障预警方法，通过神经网络预测时间节点理论健康值，与实际值比较从而识别异常。首先提取实时数据库中健康的历史运行数据，然后对其进行一系列预处理，最后作为训练数据建立贝叶斯BiLSTM预测模型。采用交叉验证方法留出测试集，使用拟合优度、均方误差以及文中提出的贝叶斯假设检验方法对模型精度进行综合验证。保证模型泛化能力后，对实时数据库中的数据进行实时预测，并采用阈值法对故障蠕变期间的异常信号进行识别。实验结果表明：所建立的贝叶斯BiLSTM预测模型在时延高的情况下，相比于LSTM模型具有更优的预测精度；在案例1中，针对3次时间序列异常点，文中模型相比于现有的实时监控系统至少提前了15h发现信号异常并进行报警；案例2的预测结果进一步验证了模型的可靠性。

The research on fault early warning method of large-scale nuclear power equipment has far-reaching significance for timely troubleshooting, reducing safety risks, cutting unnecessary costs and improving power generation efficiency. However, most of the traditional fault early warning systems of nuclear power equipment are real-time monitoring and alarm systems and havent made full use of the data value, so their early warning effects need to be improved. This paper proposed a fault early warning method based on Bayesian LSTM. The theoretical health value of time node was predicted by neural network and was compared with the actual value to find out the abnormal value. Firstly, the healthy historical operation data was extracted from the real-time database, and then a series of preprocessing were carried out on these data. Finally, the processed data was used as training set to establish the LSTM prediction model. The cross validation method was used to reserve the test set, and the goodness of fit, mean square error and Bayesian hypothesis test method proposed in this paper were used to comprehensively verify the accuracy of the model. After the generalization ability of the model was guaranteed, the data in the real-time database was predicted in real time, and the abnormal signals during fault creep were identified by threshold method. The experimental results show that the Bayesian-BiLSTM prediction model has better prediction accuracy than the LSTM model in the case of high time delay. In case 1, for the three time series outliers, the model proposed in this paper finds the abnormal signal and gives an alarm at least 15 hours in advance compared with the existing real-time monitoring systems, and its reliability is further verified by case 2.