基于全局-局部欧拉弹性判别投影的旋转机械故障诊断方法  

Fault diagnosis method of rotating machinery based on global-local Euler elastic discriminant projection

作　　者：苏树智 张茂岩 方贤进[1,2] 朱彦敏 SU Shuzhi;ZHANG Maoyan;FANG Xianjin;ZHU Yanmin(School of Computer Science and Engineering,Anhui University of Science&Technology,Huainan 232001,China;Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Hefei 230031,China;School of Computer Mechanical Engineering,Anhui University of Science&Technology,Huainan 232001,China)

机构地区：[1]安徽理工大学计算机科学与工程学院,安徽淮南232001 [2]合肥综合性国家科学中心人工智能研究院,合肥230031 [3]安徽理工大学机械工程学院,安徽淮南232001

出　　处：《振动与冲击》2023年第11期65-74,共10页Journal of Vibration and Shock

基　　金：国家自然科学基金(61806006);中国博士后科学基金(2019M660149);安徽高校协同创新项目(GXXT-2021-006)。

摘　　要：故障诊断方法通常对异常值敏感,并且难以同时提取全局和局部判别信息,从而导致低维判别特征子集类间分离性不佳,针对该问题提出了一种基于全局-局部欧拉弹性判别投影(global-local euler elastic discriminant projection,GLEEDP)的旋转机械故障诊断方法。该方法通过余弦度量将高维故障特征映射到欧拉表示空间,扩大异类故障样本间的差异,然后在该空间中构建了基于全局、局部及类间散布三个目标函数的最优化模型,实现了在保持整体结构的基础上,进一步提高低维判别特征子集的局部类内聚集性和全局类间分离性。在轴承和齿轮箱两个机械故障数据集上的试验结果表明,所提方法可以有效发掘故障判别信息,具有优越的故障诊断性能。Fault diagnosis methods are usually sensitive to outliers,and it is difficult to extract both global and local discriminant information at the same time,resulting in poor separation between low-dimensional discriminant feature subsets.To solve this problem,a fault diagnosis method of rotating machinery was proposed based on global-local Euler elastic discriminant projection(GLEEDP).The high-dimensional fault features were mapped to the Euler representation space through the cosine metrics,and the differences between heterogeneous fault samples were expanded.Then,an optimization model based on three objective functions of global,local and within-class scatter was constructed in this space,which further improved the local intra class aggregation and global inter class separation of low dimensional discriminant feature subsets on the basis of maintaining the overall structure.The experimental results on two mechanical fault datasets of bearing and gearbox show that the proposed method can effectively explore the fault discrimination information and has superior fault diagnosis performance.

关 键 词：余弦度量 欧拉表示 维数约简 故障诊断 

分 类 号：TH165.3[机械工程—机械制造及自动化]