| 引用本文: | 朱彦敏1,3,苏树智2,3.鉴别流形敏感的跨模态轴承故障诊断方法(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2024，41（3）：113-118 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
|
| 摘要: |
| 目的 在实际应用中采集的原始多模态故障数据通常是包含大量噪声和冗余信息的非线性数据,如何从不
同故障模态中提取有效的非线性故障特征仍是一个挑战性的问题。 方法 提出了一种鉴别流形敏感的跨模态故障
诊断方法,在该方法中首先借助相关分析理论在跨模态故障空间中构建了不同模态间的相关系数,并通过理论推
导获得了相关系数的等价优化模型,然后利用局部近邻图构建了鉴别流形敏感散布,进而通过最大化不同模态间
的相关性和最小化鉴别流形敏感散布,形成了鉴别流形敏感的跨模态故障诊断模型,并且在理论上推导出了该优
化模型的解析解,从而能够从不同模态的故障数据中学习强鉴别力的非线性故障特征。 结果 在德国帕德博恩轴承
数据集和多模态轴承故障数据集上设计了针对性实验,实验结果显示在少量故障样本用于训练时即可获得良好的
诊断准确性。 结论 提出的方法是一种有效的跨模态故障诊断方法。 |
| 关键词: 故障诊断 跨模态故障特征抽取 鉴别流形结构 |
| DOI： |
| 分类号: |
| 基金项目: |
|
| Discriminant Manifold Sensitivity Cross-Modal Bearing Fault Diagnosis Method |
|
ZHU Yanmin1 3, SU Shuzhi2 3
|
|
1. School of Mechanical and Electrical Engineering Anhui University of Science & Technology Anhui Huainan 232001
China
2. School of Computer Science and Engineering Anhui University of Science & Technology Anhui Huainan 232001
China
3. Joint Research Center for Occupational Medicine and Health of Institute Health and Medicine Hefei Comprehensive
National Science Center Anhui University of Science & Technology Anhui Huainan 232001 China
|
| Abstract: |
| Objective Raw multi-modal fault data collected in practical applications is usually nonlinear data containing a
large amount of noise and redundant information. How to extract effective nonlinear fault features from different fault
modalities is still a challenging problem. Methods A discriminant manifold sensitivity cross-modal fault diagnosis method
was proposed. In the method the correlation coefficient between different modalities was first constructed in the cross-modal fault space using correlation analysis theory and the equivalent optimization model of the correlation coefficient was
obtained by theoretical derivation. Then the discriminant manifold sensitivity scatter was constructed by using local
neighborhood graphs and a discriminant manifold sensitivity cross-modal fault diagnosis model was constructed by
maximizing the correlation between different modalities and minimizing the discriminant manifold sensitivity scatter. The
analytical solutions of the optimization model were derived theoretically so that nonlinear fault features with well
discriminant power can be obtained from fault data of different modalities. Results Targeted experiments were designed on
the Germany Paderborn bearing dataset and the multi-modal bearing fault dataset. The experimental results showed that
good diagnosis accuracy can be achieved with a small number of training fault samples. Conclusion The proposed method
is an effective cross-modal fault diagnosis method. |
| Key words: fault diagnosis cross-modal fault feature extraction discriminant manifold structure |
