基于无监督跨模态欧拉判别空间的旋转机械故障诊断方法  

Fault Diagnosis Method of Rotating Machinery Based onUnsupervised Cross-modal Euler Discriminant Space

作　　者：陈见 苏树智 朱彦敏 CHEN Jian;SU Shu-zhi;ZHU Yan-min(School of Computer Science and Engineering,Anhui University of Science&Technology,Huainan 232001,China;School of Mechanical and Electrical 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,淮南232001 [2]安徽理工大学机电工程学院,淮南232001

出　　处：《科学技术与工程》2025年第11期4621-4628,共8页Science Technology and Engineering

基　　金：安徽理工大学青年基金(重点项目)(QNZD202202);安徽理工大学医学专项培育项目(重大项目)(YZ2023H2A007);淮南市指导性科技计划(2023142,2023147);国家自然科学基金面上项目(52374155);安徽省自然科学基金(面上项目)(2308085MF218);安徽省高等学校自然科学研究项目(重大项目)(2022AH040113);安徽省高校中青年教师培养行动项目(YQZD2023035);合肥综合性国家科学中心大健康研究院职业医学与健康联合研究中心项目(OMH-2023-05,OMH-2023-24)。

摘　　要：在无监督情况下,进行跨模态高维故障数据高精度的故障诊断是一个挑战性问题,针对该问题,提出了一种基于无监督跨模态欧拉判别空间的旋转机械故障诊断方法(unsupervised cross-modal Euler discriminant space, UCEDS)。在该方法中,跨模态故障数据样本通过余弦度量映射到欧拉表示,增强不同类型故障样本之间的差异性和可分性,然后在该空间中构建无监督跨模态欧拉判别空间学习模型,在理论上推导出了模型的解析解。该模型不仅考虑了故障样本的局部邻域结构,能够有效地发现复杂和非线性故障特征样本的局部结构信息,同时,在跨模态一致判别融合的基础上,进一步提高了低维判别特征子集模态间的互补性。在帕德博恩故障轴承数据集上的针对性实验表明,本文提出的UCEDS方法具有优越的故障诊断分类性能。The high-precision fault diagnosis of cross modal high-dimensional fault data under unsupervised conditions is a challenging problem.To address this issue,a rotating machinery fault diagnosis method based on unsupervised cross-modal Euler discriminant space(UCEDS)was proposed.In this method,cross-modal fault data samples were mapped to Euler representations through cosine metrics to enhance the differences and separability between different types of fault samples.Then,an unsupervised cross modal Euler discriminant space learning model was constructed in this space,and the analytical solution of the model was theoretically derived.This model not only considered the local neighborhood structure of fault samples,but also effectively discovered the local structural information of complex and nonlinear fault feature samples.At the same time,on the basis of cross modal consistent discriminative fusion,it further improved the complementarity between low dimensional discriminative feature subsets.Targeted experiments on the Paderborn fault bearing dataseht showed that the proposed UCEDS method had superior fault diagnosis and classification performance.

关 键 词：滚动轴承 故障诊断 跨模态 欧拉表示 维数约简 

分 类 号：TP249[自动化与计算机技术—检测技术与自动化装置]