主成分分析在发动机状态监控与故障诊断中的应用

讨论了主成分分析在发动机状态监控与故障诊断中的应用，即利用主成分分析对近似线性相关的多个向量进行降维的问题。特别讨论了一般文献中很少注意的两个特殊问题，即测量数据的等方差化问题和中心化限制问题。以ＪＴ９Ｄ发动机故障、故障系数向量以及ＪＴ９Ｄ和ＰＷ４０５６发动机维修指标的降维问题为例说明主成分分析的应用。研究结果表明，利用主成分分析可以将ＪＴ９Ｄ发动机的２６个故障因子综合成９个、或者将５个单元体的１０个故障因子缩减到５个综合变量，并且改善了故障方程的病态特性，因而可以有效地提高故障诊断的可靠性。文中还对用于降维处理的主成分分析方法与线性回归分析（最小二乘法）进行了比较，表明主成分分析方法是解决所提出的降维问题的更为有效的手段。

The Application of Principal Components Analysis to Engine Condition Monitoring and Fault Diagnosis

The paper presents the dimension reduction of multiple linearly dependent vectors by using the principal components analysis in engine condition monitoring and fault diagnosis, discussing the principles of iso-variance transformation and decentralization of original vectors and demonstrating the application of the given method in the JT9D and PW4056 engines The investigation shows that the 26 fault factors of JT9D engine can be reduced to 9, or the 10 fault factors of 5 engine modules can be reduced to 5 combined variables, thus improving the abnormal condition of the engine fault equation and obtaining better diagnostic results