中文摘要：

在对飞机发动机转子系统早期故障特点进行分析的基础上,针对其故障诊断中存在的故障样本不足和早期微弱故障不易识别的问题,提出将随机共振、小波包分析与支持向量机相结合的发动机转子系统早期故障诊断与智能自愈监控方法。该方法首先利用随机共振原理对早期微弱故障信号进行特征细化,使故障特征放大;然后利用小波包多分辨率分析特性进行故障特征提取;再将提取的特征向量输入由支持向量机构造的分类器中进行故障识别,并利用智能自愈方法对故障进行监控。对智能诊断系统结构、故障特征提取方法、多故障分类器构造、故障自愈监控等进行了分析和研究。结果表明,该方法在故障样本不足情况下,能有效识别发动机转子系统的早期故障,且算法简单、故障分类识别效果好,并能对故障进行自愈监控。

英文摘要：

Generally, there isn＇t a large number of fault samples in early aero-engine fault diagnosis and early weak fault signals is hard to identify. In order to solve this problem, a new method for early fault identification on an aero-engine rotor system （AERS） is proposed which is able to implement fault intelligent self-recovery monitoring （FISRM）. This method is based on the analysis of early fault characteristics of AERS, and it combines the support vector machine （SVM） with the stochastic resonance theory （SRT） and wavelet packet decomposition （WPD）. First, the SRT is employed to zoom the early fault feature signals. Then, the multi-resolution analysis of the wavelet packet is used to extract early fault features. Finally, the early fault feature vector is inputted to the classifier, and the early fault of AERS can be identified and monitored by intelligent self- recovery. In this article, the structure of the intelligent diagnosis system, the method of extracting early fault characteristics, the construction of multi-fault classifier and the FISRM are analyzed. The results show that the proposed method can identify an early fault of AERS in small samples, and its algorithm is simple, the identification effect is satisfactory, and an early fault of AERS can be monitored by self-recovery.