[1]张 磊,缪秋循,刘 川,等.判别结构保持的跨工况故障诊断方法[J].机械与电子,2026,44(09):1-8.
　ZHANG Lei,MIAO Qiuxun,LIU Chuan,et al.Cross-condition Fault Diagnosis Method Based on Discriminative Structure Preservation[J].Machinery & Electronics,2026,44(09):1-8.
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判别结构保持的跨工况故障诊断方法()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
44
期数:
2026年09期
页码:
1-8
栏目:
研究与设计
出版日期:
2026-09-25

文章信息/Info

Title:
Cross-condition Fault Diagnosis Method Based on Discriminative Structure Preservation
文章编号:
1001-2257(2026)09-0001-08
作者:
张 磊1; 缪秋循1; 刘 川1; 李晓成1; 顾小虎1; 余 洋1; 朱 静2; 邓艾东3
1.南京南自华盾数字技术有限公司,江苏 南京 210003;
2.河南科技大学车辆与交通工程学院,河南 洛阳 471003;
3.东南大学能源与环境学院,江苏 南京 210003
Author(s):
ZHANG Lei1; MIAO Qiuxun1; LIU Chuan1; LI Xiaocheng1; GU Xiaohu1; YU Yang1; ZHU Jing2; DENG Aidong3
(1.Nanjing Nanzi Huadun Digital Technology Co.,Ltd.,Nanjing 210003,China;
2.School of Vehicle and Transportation Engineering,Henan University of Science and Technology,Luoyang 471003,China;
3.School of Energy and Environment,Southeast University,Nanjing 210003,China)
关键词:
滚动轴承; 跨工况故障诊断; 判别结构保持; 特征融合; 图卷积网络
Keywords:
rolling bearing; cross condition fault diagnosis; discriminative structure preservation; feature fusion; graph convolutional network
分类号:
TH133.33;TP18
文献标志码:
A
摘要:
针对跨工况轴承故障诊断中源域与目标域特征分布偏移、目标域类别判别结构退化等问题,提出一种判别结构保持协同融合网络。该方法以频域振动信号为输入,利用图卷积网络时间卷积网络并联结构分别提取频谱局部关联特征和跨频带依赖特征,并通过双向交叉注意力机制实现异构特征协同融合;同时,引入基于余弦相似度的判别结构保持约束,增强跨域同类样本的特征一致性。在华中科技大学和东南大学2个轴承故障数据集上的实验结果表明,所提方法的平均迁移准确率分别达到99.71%±0.13%和99.63%±0.11%,均优于对比方法,具有较好的跨工况迁移诊断性能。
Abstract:
To address the problems of feature distribution shift between source and target domains and the degradation of the category-wise discriminative structures in the target domain for cross-condition bearing fault diagnosis,this paper proposes a discriminative structure-preserved collaborative fusion network. The proposed method takes frequency-domain vibration signals as inputs,and employs a parallel architecture comprising a graph convolutional network and a temporal convolutional network to extract locally correlated spectral features and cross-frequency-band dependency features,respectively.These heterogeneous features are then synergistically fused via a bidirectional cross-attention mechanism.Meanwhile, a discriminative structure preservation constraint based on cosine similarity is introduced to enhance the feature consistency of samples from the same class cross domains.Experimental results on two bearing fault datasets of Huazhong University of Science and Technology and Southeast University show that the proposed method achieves average transfer accuracies of 99.71%±0.13% and 99.63%±0.11%,respectively, outperforming the compared methods and exhibiting superior cross-condition transfer diagnosis performance.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2026-05-30
基金项目:国家自然科学基金资助项目(52576010)
作者简介:张 磊 (1983-),男,江苏宿迁人,硕士研究生,高级工程师,研究方向为动力机械及工程;朱 静 (1993-),女,江苏淮安人,博士研究生,研究方向为风电机组故障诊断,通信作者,E-mail:1907175086@qq.com。
更新日期/Last Update: 2026-10-12