旋转机械振动信号的信息熵特征

EXTRACTING INFORMATION ENTROPY FEATURES FOR ROTATING MACHINERY VIBRATION SIGNALS

从信息融合的思想出发 ,针对单个和多个振动传感器 ,在时域、频域以及时 -频域系统、深入地研究了定量评价旋转机械振动状态的方法 ,提出了反映不同域中振动能量分布不确定性的奇异谱熵、功率谱熵、涡动状态特征熵、小波空间特征熵等信息熵特征。通过对实际信号的分析表明 ,这些信息熵形成了有效综合评价转子振动状态的特征指标。

In the view of information fusion, the methods how to evaluate the vibration state of rotating machinery with single and multiple channel signals systematically are studied. The several spectrum entropy features in different domains are proposed. Multi-channel singular spectrum entropy reflects the indetermination degree of vibration energy distribution of spatially spreading multi-sensor signals in time domain. Whirling state feature entropy of multi-section rotor considers the complication of energy and state varying simultaneously in frequency domain. Wavelet space feature entropy characterizes complexity of feature patterns distribution in time-frequency domain. The analysis and calculation of the fault signals show that these information entropy are capable of classifying and indicating complication degree of faulty signals authentically, so they can form the characteristic index of effectively and synthetically evaluating the rotor vibrating state.