航空发动机包线内气路故障融合诊断机理及方法研究

项目名称： 航空发动机包线内气路故障融合诊断机理及方法研究

项目编号： No.61304113

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 鲁峰

作者单位： 南京航空航天大学

项目金额： 25万元

中文摘要： 先进航空发动机结构日趋复杂，发动机健康管理是保证飞行安全、降低维护使用成本的重要手段，已成为航空动力技术领域的研究热点。作为发动机健康管理最重要组成之一的气路分析技术，近年来正由状态监视向健康预测与诊断、由单一算法向融合算法方向发展，如何提高包线内全状态的气路故障诊断精度成为亟待解决的问题之一。 本项目首次提出一种基于信息融合的航空发动机包线内全状态气路性能估计与诊断方法。结合基于模型与数据驱动的发动机气路分析方法特点，探索包线内全状态的气路故障诊断融合机制，揭示包括基于非线性自适应模型的、故障特征提取及特征层融合的、决策层定性和定量融合的气路故障诊断机理，设计发动机气路故障融合诊断快速原型验证方法，以期提高发动机气路故障诊断精度，扩展适用范围，为智能航空发动机健康管理提供相关理论依据和应用基础。

中文关键词： 航空发动机；气路故障诊断；信息融合；特征提取；快速原型验证

英文摘要： Advanced aircraft engine has more complicated structure nowadays. Engine health management (EHM) is one of the essentials to achieve the condition-based maintenance, reduce the operation cost, and ensure the flight safety. Gas path analysis (GPA) for aircraft engine plays one of the most important roles in the EHM. Recently, research on the GPA has been extended from condition monitoring to health prognostics and diagnosis, from simple functional structure to fusion one. At the same time, how to improve the accuracy of the existing GPA approaches within the whole flight envelope becomes one problem to solve urgently. The gas-path fusion mechanisms and methodologies for both performance health estimation and diagnosis within the whole flight envelope are proposed. Combining the GPA characteristics both of the model-based approach and the data-driven one, explore the fusion mechanism and structure for the GPA within the whole flight envelope, including researches on nonlinear self tuning hybrid model, feature extraction and feature-level fusion, and decision-level fusion. Rapid prototype validation experiments of gas-path fault diagnosis and prognosis for aircraft engine based on information fusion are designed. Attempt to the researches on the project, gas-path performance health estimate accuracy and fault diagn

英文关键词： aircraft engine；gas-path fault diagnosis；information fusion；feature extraction；rapid prototype test