基于角域振动信号特征统计量的发动机故障分类方法

摘要

In order to improve the precision rate and success rate of engine fault classification,a new fault classification based on the characteristic statistics in angle domain was proposed.The new method used an optical encoder to acquire engine rotational vibration signals of equal angle sampling. Wavelet packet analysis and correlation coefficient method were applied to acquire the characteristic order of the engine signals in angle domain.Energy ratio,standard deviation ratio,spectrum energy ra-tio and spectrum mean ratio of the characteristic order were regarded as the characteristic statistics in angle domain to extract engine fault features.The method of support vector machines was also adopted to classify the engine faults.Experimental results using signals recorded from an engine with an im-proper fit clearance existed in the connecting rod bearing show,compared with the traditional fault classification,this new method can improve precision rate of the fault classification.%为了提高发动机故障分类的准确率和成功率，提出了基于角域信号特征统计量的发动机故障分类方法。包括：利用编码器进行发动机振动信号的等角度采样；采用小波包分析和相关系数法获取发动机角域信号的特征阶次；选取特征阶次信号的能量比、标准差比、谱能量比及谱均值比4组参数作为角域信号特征统计量来提取发动机故障特征；采用支持向量机法对发动机故障进行分类。连杆轴承配合间隙故障的台架试验结果证明：相比于传统的分类方法，该方法明显提高了发动机故障分类的准确率。