针对燃气轮机燃烧室故障预警受限于样本数据稀缺导致的低效问题，提出一种基于迁移学习和卷积神经网络-双向门控循环网络(CNN-BiGRU)的方法。首先在源域上使用K折交叉验证对CNN-BiGRU网络进行预训练并筛选出最优模型；然后使用迁移学习得到目标域故障预警模型；再将目标域数据导入该模型中并采用滑动窗口减少异常数据点造成的误报警；最后利用三倍标准差法(3-sigma)计算出的预警阈值判断是否发生故障。实验结果表明：在样本数据不足时，所提方法与非迁移学习模型相比，均方根误差降低了87.5%，平均绝对误差降低了89.05%，决定系数提升了6.39%；与系统报警时刻相比，能够更早发现故障征兆，提前82 min对燃气轮机燃烧室进行故障预警。

Aiming at the inefficiency of gas turbine combustion chamber fault warning due to the scarcity of sample data, a method based on transfer learning and convolutional neural networkbidirectional gated recurrent network (CNN-BiGRU) is proposed. Firstly, the CNN-BiGRU network is pre-trained in the source domain using K-fold cross validation and the optimal model is filtered out; then the target domain fault warning model is obtained using transfer learning; then the target domain data is imported into the model and a sliding window is used to reduce the false alarms caused by the abnormal data points; finally, the warning thresholds computed using the triple standard deviation (3-sigma) method are used to determine whether a fault occurs. The experimental results show that compared with the non-transfer learning model, the proposed method reduces the root mean square error by 87.5%, the mean absolute error by 89.05%, and improves the r-squared by 6.39% when there is insufficient sample data; and compared with the system alarm moment, it is able to detect the signs of faults earlier, and warn the gas turbine combustion chamber of faults 82 min in advance.