The hob forms key component in gear hobbing machine and its health condition directly affects the reliability and safety of entire machine. This paper proposes a multiscale feature extraction scheme based on spectral graph wavelet transform combined with improved random forest, forming a novel hob fault diagnosis technique and realizing multi-scale analysis of vibration signals from the perspective of graph. Firstly, the vibration signal samples are transformed into path graphs, which contain the vertices information and similarity information between connected vertices, enriching the input information. Then, the path graphs are preprocessed by spectral graph wavelet transform at five-level decomposition for feature extraction. Finally, the random forest improved by adaptive beetle antennae search is utilized to identify the hob fault. Two groups of experimental results indicate that the proposed method has high effectiveness and robustness, achieving all the identification accuracy greater than 90% under multiple operating conditions and various environmental noises.

滚刀是滚齿机的关键部件，其健康状况直接影响着整机的可靠性和安全性。提出了一种基于谱图小波变换结合改进的随机森林的多尺度特征提取方案，形成了一种新型的滚刀故障诊断技术，并从图的角度实现了振动信号的多尺度分析。首先，将振动信号样本转换为路径图，其中包含顶点信息和连接的顶点之间的相似度信息，从而丰富了输入信息。然后，通过频谱图小波变换对路径图进行五级分解预处理，以进行特征提取。最终，利用自适应甲虫天线搜索改进的随机森林来识别滚刀故障。