本文通过分析活塞销在不同配合间隙下的运行轨迹，针对活塞销敲击响产生缸盖振动信号的有序性和在频率成分上的差异性，并考虑到实验数据的有限性，提出了用香农熵选取小波包子信号的特征提取方法和支持向量机的故障诊断方法。将振动信号进行小波包分解并求出各子信号的香农熵，根据香农熵的大小选取出合适的子信号进行分析研究，求出子信号的能量作为特征值，用支持向量机对活塞销不同程度的故障进行分类识别。诊断结果表明：用香农熵选取小波包子信号结合支持向量机的方法能够对活塞销不同程度的故障进行分类识别。
Abstract
Here, through analyzing operational trajectories of a piston pin under different fit clearances, aiming at the orderliness of cylinder cover vibration signals generated due to striking the piston pin and their difference in frequency components, considering the finiteness of test data, a method for choosing a wavelet packet’s sub-signals with Shannon entropy, extracting their features and then diagnosing faults with SVM was proposed.Firstly, a vibration signal of the piston pin was decomposed into several sub-signals with the wavelet packet transform and Shannon entropy of each sub-signal was calculated.According to Shannon entropy values, appropriate sub-signals were chosen and their energy was calculated and taken as their features.A support vector machine (SVM) was used to classify the piston pin’s different faults.The diagnosis test results showed that the proposed method can be used to identify and classify different faults of the piston pin.
关键词
香农熵 /
小波包 /
支持向量机 /
故障诊断
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Key words
Shannon entropy /
wavelet packet /
support vector machine /
fault diagnosis
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