【作者】 张琰；

【导师】 李忠海；

【作者基本信息】 沈阳航空工业学院 ， 模式识别与智能系统， 2010， 硕士

【摘要】 航空发动机是飞机的重要组成部分,它的微小故障也会直接导致飞机重大事故的发生。由于航空发动机具有结构复杂和工作条件恶劣等特点,所以航空发动机的故障诊断是目前研究的重点之一。本文以涡扇发动机的气路故障为研究目标,利用多元统计分析及其改进算法得到一个综合性能参数来表征单元体的性能特征用来分析整体性能状态,对发动机状态实时监测。当故障发生时,利用独立元作为训练样本建立最小二乘支持向量机模型定位故障,为实现单元体的视情维修提供了理论依据。主要研究内容有以下三个方面。首先,研究了基于基本多元统计分析的发动机故障检测,验证了多元统计分析方法中主元分析(PCA)和核主元分析(KPCA)的应用可行性,并提出基于独立元分析(ICA)的航空发动机故障检测方法。其次,提出了基于多尺度独立元分析(MSICA)的航空发动机故障检测方法,将小波分析和ICA结合起来应用到航空发动机故障诊断领域中。最后,提出了基于ICA-LSSVM方法的航空发动机故障定位,该方法将独立元分析与最小二乘支持向量机相结合,实现了将航空发动机故障定位到各个单元体。仿真实验结果表明:基于ICA的航空发动机故障检测方法是简单有效的;MSICA方法可以进一步提高发动机故障检测系统发现较小幅度异常的能力;基于ICA-LSSVM的故障定位是可行的,比传统方法速度快,准确率高。更多还原

【Abstract】 The aero-engine, whose slight fault will cause fatal accident directly, is important component of airplane. Moreover, the aero-engine’s structure is complex and works in harsh environment, so the research of aero-engine fault diagnosis is a focal point. In this thesis, Multivariate statistical analysis is used to obtain an overall performance parameter in order to monitor the turbofan engine’s gas path. When fault occurs, Least Squares Support Vector Machine (LSSVM) is used to implement fault accommodation, which provides the theory to component’s condition based maintenance.The main contents are as follows. First, Aeroengine fault diagnosis based on multivariate statistical analysis is studied. Principal Component Analysis (PCA), Kernel Principal Components Analysis (KPCA) and Independent Component Analysis (ICA) are validated. Second, Multi-scale Independent Component Analysis (MSICA), which combines wavelet transform with ICA to detect aeroengine path fault, is introduced into fault diagnosis. Third, ICA-LSSVM is introduced into aeroengine fault accommodation. ICA and Least Squares Support Vector Machine (LSSVM) is combined to identify fault component. Simulation results show that ICA is effective, MSICA methods can detect slight fault further, and Fault accommodation method base on ICA-LSSVM is feasible.更多还原