作　　者： ; ; ; ; ; ;

机构地区： 空军工程大学航空航天工程学院

出　　处： 《空军工程大学学报（自然科学版）》 2013年第4期9-13,共5页

摘　　要： 为提高故障特征提取的准确性,将随机共振算法用于振动信号的预处理,在此基础上进行故障特征的提取。首先介绍了随机共振的降噪原理,并对适用于大参数信号的变尺度随机共振进行了分析,提出一种快速的频率压缩比R的寻优方法;为了验证本文提出的特征提取方法,分别提取了基于时域、频域和时频域的振动故障特征集;最后,应用类内类间离散度指标对故障集的分类性能进行了分析。分析结果表明,由随机共振输出信号提取得到的特征集的分类指标要明显优于原始信号提取的特征集,特征提取的准确性得到显著提高。 In order to improve the accuracy of fault feature extraction, the stochastic resonance （SR） meth od is proposed in the pretreatment of vibration signals, then the fault feature is extracted based on the method. First, the denoising principle of SR is presented, and the mutable scale SR, which is suitable for large parameter signal, is analyzed. Then a fast optimization method of frequency compression ratio R is put forward. The vibration fault feature sets based on time domain, frequency domain, timefrequency do main are extracted respectively to test the proposed feature extraction method. Finally, the discrete degree index based on betweenclass and withinclass is applied to analyze the classification performance of feature set. The analysis result shows that the classification indexes of the feature sets extracted from SR output signal are obviously superior to those from the original signal, the feature extraction accuracy is improved notably.

关 键 词： 故障特征提取 随机共振 预处理 可分性分析

领　　域： [航空宇航科学与技术] [航空宇航科学技术]