基于SDP图像与VGG网络的旋转机械转子故障诊断研究  

Rotor fault diagnosis of rotating machinery based onSDP images and VGG network

作　　者：武海彬 卜明龙 刘圆圆 郝惠敏 WU Hai-bin;BU Ming-long;LIU Yuan-yuan;HAO Hui-min(School of Mechanical and Vehicle Engineering,Taiyuan University of Technology,Taiyuan 030024,China)

机构地区：[1]太原理工大学机械与运载工程学院,山西太原030024

出　　处：《机电工程》2020年第9期1069-1074,共6页Journal of Mechanical & Electrical Engineering

基　　金：山西省重点研发计划(高新领域)项目(201903D121002);山西省重点研发计划(国际科技合作)项目(201603D421009)。

摘　　要：针对传统故障诊断方法对旋转机械转子故障状态识别精度较低的问题,提出了一种基于对称点模式图像特征信息融合与深度学习相结合的旋转机械转子故障诊断方法。采用SDP信息融合技术,对转子故障状态下的多通道振动信号进行了信息融合,通过SDP图形特征可简单直观地区分不同转子故障振动状态;结合深度学习VGG网络自适应提取了SDP图像的特征信息,对不同故障转化的SDP图像实现了准确的诊断识别,进而判别了其故障类型;通过变速器机械故障模拟实验验证了所提出方法的有效性,并与传统机器学习方法极限学习机(ELM)进行了比较。研究结果表明:基于SDP图像与VGG网络的旋转机械转子故障诊断方法解决了转子故障振动信号中存在的高复杂、非线性和不稳定问题,与传统机器学习方法ELM相比具有更高的识别精度。Aiming at improving the identification accuracy of rotating machine rotor fault diagnosis,a deep learning visual geometry group(VGG)network method combined with symmetry dot pattern(SDP)image feature was proposed.The multi-channel vibration signals of rotor in fault state were transformed by SDP,and the SDP images of different rotor fault shown distinguish features.The SDP images were input the deep learning VGG network adaptively,and the fault features were extracted by VGG.The results show that the SDP images combined with VGG obtained more accurate diagnosis and fault recognition of rotor fault than extreme learning machine(ELM).The results indicate that the method for fault diagnosis of rotating machinery rotors based on SDP images and VGG networks solves the problems of high complexity,nonlinearity and instability in vibration signals of rotor faults,and has higher recognition accuracy than traditional machine learning methods ELM.

关 键 词：深度学习 VGG网络 SDP图像 多通道信息融合 转子故障诊断 极限学习机 

分 类 号：TH132.46[机械工程—机械制造及自动化] TH113.1