多振动信号的时频相干多分形特征提取
Extraction of Time-Frequency Coherence Multifractal Features for Multiple Vibration Signals

为充分利用多个同步采样的振动信号进行机械设备的故障诊断，提出了搭建多振动信号时频相干网络并提取其多分形特征的方法。首先，将每个振动信号作为一个节点，根据所关心的物理问题，按适当的方式将各个节点连接成网；其次，对网络中相邻的每对节点做交叉小波变换，得到时频相干谱，借助小波领袖来估计时频相干谱的多分形谱，用曲线拟合的方法来提取多分形谱的形态特征；最后，利用特征融合与维数约简方法，对已得到的所有特征进行融合和降维，从而得到整个网络的最终特征。该方法给出了一个提取多振动信号时频相干多分形特征的框架，并在某高射机枪自动机的裂纹故障诊断中取得了成功应用，具有广泛的适用范围。
In order to make full use of synchronously sampled vibration signals for the fault diagnosis of mechanical equipment, a method for building time-frequency coherence network of vibration signals and extracting the multifractal features of the network is proposed. It takes each vibration signal as a node. According to the physical issues of concern, the nodes are connected to form a network with proper structure. Then, cross wavelet transform is performed for each pair of adjacent nodes in the network, and the time-frequency coherence spectra are obtained. Wavelet leaders are used to estimate the multifractal spectra of the time-frequency coherence spectra. The morphological features of the multifractal spectra are extracted by curve fitting. Finally, using feature fusion and dimension reduction methods, all the features obtained are fused with reduced dimension, and the final features of the whole network are obtained. This method proposes a framework for the extraction of time-frequency coherence multifractal features of multiple vibration signals. It has been successfully applied to the crack fault diagnosis for the automatic mechanism of an antiaircraft machine gun, and has a wide scope of applications.