【摘要】 为提高航天发射任务的可靠性和安全性，对液体火箭发动机异常智能检测技术进行了研究。针对传统方法存在的检测准确率低、依赖专家经验和先验知识、所需数据量大等问题，提出运用深度学习方法构建自编码式—生成对抗网络——训练基于发动机健康状态下的多源数据，测试基于输入数据的重构损失和鉴别分数，完成对液体火箭发动机异常状态的智能检测。某型号液体火箭发动机地面热试车实验数据的分析结果表明，该方法取得了96.55%的测试准确率，并在利用邻近信息的条件下取得最高100%的准确率，可有效用于液体火箭发动机的异常检测。更多还原

【Abstract】 To improve the reliability and safety of the space launch mission, the intelligent anomaly detection technology of liquid rocket engine was studied.Aiming at the problems of traditional methods such as low detection accuracy, relying on expert experience and prior knowledge, and large amount of data needed, a deep learning was proposed to build an autoencoding generative adversarial network to train the multi-source data under the engine health state.The reconstruction loss and discriminant score of input data were tested, and the intelligent anomaly detection of liquid rocket engine was completed.The analysis results of the ground hot test data for a liquid rocket engine show that this method achieves 96.55% test accuracy, and the 100% accuracy can be obtained by using the neighboring information.It can be effectively used for the anomaly detection of liquid rocket engine.更多还原