| 引用本文: | 侯雅魁,苏树智,张志鹏.基于曼哈顿局部-全局判别空间学习的滚动轴承故障诊断方法(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2025，42（6）：55-62 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 滚动轴承作为机械设备的核心部件,在长时间运转下会出现磨损、变形。 针对现有方法判别轴承处于
何种故障困难的问题,提出一种基于曼哈顿局部-全局鉴别空间学习的故障诊断方法。 方法 该方法具有更丰富的
潜在流形结构,使用曼哈顿距离重构原始空间图结构;通过构造局部类内和类间图发掘潜在鉴别信息和局部信息,
并在原始全局结构的基础上增加全局类内和类间图,提高了类间分离性和类内聚合性。 首先,将原始故障信号进
行特征提取得到特征测试集和特征训练集;然后,将特征训练集输入曼哈顿局部-全局鉴别空间学习模型中,提取
原始空间中局部信息、全局结构和类别信息;接着,通过求解该模型可以得到空间投影的解析解;最后,将得到的空
间投影解析解与特征测试集输入支持向量机中进行故障分类。 结果 实验结果表明:所提方法在搭建的轴承故障平
台上表现出良好的性能,最终故障识别率为 94. 23%。 结论 文中提出的方法在轴承故障诊断方面表现出较高的识
别率,为轴承故障诊断带来了重要的进展,具有深远的意义。 |
| 关键词: 故障诊断 曼哈顿距离 流形缺失 空间学习 |
| DOI： |
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| Rolling Bearing Fault Diagnosis Method Based on Manhattan Local-global Discriminative Space Learning |
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HOU Yakui SU Shuzhi ZHANG Zhipeng
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School of Computer Science and Engineering Anhui University of Science and Technology Huainan 232001 Anhui
China
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| Abstract: |
| Objective As the core component of mechanical equipment rolling bearings suffer from wear and deformation
during long-term operation. Addressing the difficulty of existing methods in distinguishing bearing faults a fault diagnosis
method based on Manhattan local-global discriminative space learning is proposed. Methods The method has a richer
underlying manifold structure and uses the Manhattan distance to reconstruct the original space graph structure. By
constructing local intra-class and inter-class graphs it extracts potential discriminative and local information.
Additionally global intra-class and inter-class graphs are introduced based on the original global structure enhancing
inter-class separability and intra-class cohesion. Firstly feature extraction is performed on the original fault signals to
obtain feature testing and training sets. Then these feature training sets are inputted into the Manhattan local-globaldiscriminative space learning model to extract local information global structure and category information from the
original space. Next by solving this model an analytical solution for spatial projection can be obtained. Finally the
obtained analytical solution for spatial projection is input into a support vector machine along with the feature testing set for
fault classification. Results Experimental results demonstrate that the proposed method exhibits excellent performance on
the constructed bearing fault platform achieving a final fault recognition rate of 94. 23%. Conclusion The method
proposed in this paper shows high recognition accuracy in bearing fault diagnosis marking significant progress in this field
and carrying profound implications. |
| Key words: fault diagnosis Manhattan distance missing manifolds spatial learning |
