基于多分辨率近似熵的发动机声信号特征提取

介绍了近似熵的概念、主要特点及其快速算法,在分析了小波包与近似熵原理的基础上,提出了一种多分辨率近似熵的特征提取方法,并且讨论了近似熵值计算中3个参数的选择原则,其后对发动机声信号进行了分析处理,通过对比正常状态与故障状态共8种工况下的小波包3层分解后各节点的近似熵值,确定出了故障的特征频带,根据近似熵在敏感频带内的变化有效提取出发动机故障特征,从而实现了对发动机状态的监测与诊断。试验结果也证明了近似熵在分析复杂信号特征方面具有很强的能力,在判别机械设备运行状况方面具有很好的效果。

Feature Extraction of Engine Acoustic Signal Based on Multi-resolution Approximate Entropy

The concept, main feature and fast algorithm of approximate entropy (ApEn) were introduced firstly. Then by analyzing the principle of wavelet packet and ApEn, the feature extraction method of multi-resolution approximate entropy was put forward and the selection principle of three parameters was simultaneously discussed during the course of the ApEn calculation. Subsequently, the engine acoustic signal was analyzed and processed. Further, the fault feature frequency band was confirmed by comparing the ApEn values of three layers wavelet packet decomposition under eight working conditions of normal state and fault state, and hence the engine fault features could be effectively extracted according to the variation of ApEn in sensitive frequency band. Finally, the inspection and diagnosis of engine were realized. The experiment results also proved that ApEn had the good ability of analyzing the complex signal feature and was an effective method to identify the running condition.