Abstract: In the parameter fault detection during rocket flights, the traditional red line method has high missing alarm rate and false alarm rate, expert system method has high maintenance cost, and machine learning is constrained by dataset size so that it is hard to train the model. Therefore, two transfer learning strategies based on instance and model respectively were proposed. In order to realize the real-time detection of the key parameter oxygen pump speed in the new type engine YF-77, after analyzing the parameters and data characteristics of LOX/LH2 engines YF-75 and YF-77 that have the same construction principle, the domain differences were solved, the feature space was built, and the feature vectors were filtered. In the experiments about instance transfer and model transfer from YF-75 to YF-77, compared with the methods without transfer learning such as k-Nearest Neighbor (kNN) and Support Vector Machine (SVM), the models after transfer learning can reduce the missing alarm rate from 58.33% (the highest) to 12.25% (the lowest), and reduce the false alarm rate from 60.83% (the highest) to 13.53% (the lowest), therefore verifying the information transferability between two kinds of engines, and the possibility of applying transfer learning in aerospace engineering practice.

Key words: aerospace Telemetry, Track and Command (TT&C), rocket engine, parameter fault detection, transfer learning, data processing

摘要： 在火箭飞行时的参数异常检测中，传统红线法的漏报率和误报率较高，专家系统法的维护成本过高，而机器学习受制于数据集规模难以训练模型，因此提出了分别基于实例和基于模型的两种迁移策略。为了对YF-77新型发动机的关键参数氧泵转速进行实时监测，在分析具有相同构造原理的YF-75与YF-77氢氧发动机的参数组成和数据特点后，处理领域差异，构建特征空间，并筛选特征向量。对YF-75向YF-77分别进行了实例和模型的迁移，并进行了实验验证。对比无迁移的k最近邻（kNN）与支持向量机（SVM）方法，迁移训练后的模型的漏报率从最高58.33%降至最低12.25%，误报率从最高60.83%降至最低13.53%。实验结果验证了两型发动机之间信息的可迁移性，以及迁移学习在航天领域工程实践中应用的可能性。

关键词: 航天测控, 火箭发动机, 参数异常检测, 迁移学习, 数据处理