飞行器监测信号数据分析问题是近期飞行器故障检测与健康管理领域的研究热点问题，也是一个典型的模式识别与分类问题。随着测试技术的提高，需要解决测试数据量大，特征维数高，传统识别方法效率低的问题。本项目采用深度学习网络模型的方法对电信号进行特征识别与分类。首先提出模糊聚类与专家知识结合的标准样本库建立方法，采用受限玻尔兹曼机深度置信网络构建深层神经元模型，进一步提出迁移学习与模糊微调优化的方法和在线集成学习的分类方法。伴随着从无监督初步学习到有监督精细学习的过程，完成对设备电性能动态响应信号数据从浅层学习到深层学习的飞跃 。并揭示出电信号故障事件的特征，使系统对正常事件与异常事件进行精确的识别，尽可能的减少人工干预和误报率。最后，总结出评价电信号波形相似度和学习算法验证与评估的方法。可为飞行器测试提供可靠的分析方法，保证飞行器研制工作顺利进行，为智能化故障诊断与健康管理技术提供技术支撑。

Recently the analysis of aircraft surveillance signal data is a hot issue in the field of aircraft fault detection and health management.In fact it is a typical pattern recognition and classification problem. With the development of testing technology, it is necessary to solve such problems as large amount of test data, high feature dimension and low efficiency of traditional identification methods. In this project, the deep learning network model is adopted to identify and classify the electrical signals. Firstly the establishment of standard sample database using fuzzy clustering and expert knowledge will be proposed. Secondly, the RBM deep belief network construction using deep neuron model will put forward the classification method of migration method and fuzzy tuning optimization of online ensemble learning. With the process of supervised fine learning, the dynamic response of equipment electrical performance which is from shallow learning to deep learning has been completed. It also reveals the characteristics of electrical signal fault events, so that the system can identify the normal events and abnormal events accurately, and reduce the rate of manual intervention and false positives as much as possible. Finally, this methods of evaluating the similarity of electrical signals and the algorithms for verification and evaluation of learning algorithms are summarized. It can provide a reliable analysis method for aircraft testing, and ensure the development of aircraft. It can provide technical support for intelligent fault diagnosis and health management technology.