| 雷杨博,朱智勤,柴 毅,齐观秋,安翼尧,徐 鹏.基于联合分布偏移差异的跨域滚动轴承
故障诊断方法[J].电子测量与仪器学报,2022,36(10):146-156 |
| 基于联合分布偏移差异的跨域滚动轴承
故障诊断方法 |
| Cross-domain fault diagnosis method of rolling bearings basedon joint distribution offset difference |
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| DOI： |
| 中文关键词: 跨域故障诊断 域自适应 分布对齐 卷积神经网络 |
| 英文关键词:cross-domain fault diagnosis domain adaptation discrepancy alignment CNN |
| 基金项目:国家自然科学基金(61803061, 61906026)、重庆市教委重庆市高校创新群体“成渝双城经济圈建设”科技创新项目（KJCXZD2020028）、重庆市技术创新与应用发展专项（cstc2019jscx zdztzx0068）项目资助 |
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| 中文摘要: |
| 现有的无监督域自适应故障诊断方法大多只基于单一域信号实现,提取的故障信息不够全面。 只注重实现源域和目标
域特征的边缘分布对齐,忽略了样本的条件分布差异,限制了诊断精度的提升。 为克服以上问题,提出一种基于联合分布偏移
差异(joint distribution offset difference, JDOD)的跨域滚动轴承故障诊断方法。 使用两个结构一致的卷积神经网络(CNN)分别
提取信号的时域与频域特征,获取更完整的故障信息。 提出联合分布偏移差异,实现不同域特征的边缘分布对齐和条件分布对
齐。 在两个多工况轴承数据集上与多种先进方法展开对比实验,取得了 99%以上的平均诊断精度。 实验结果表明联合分布偏
移差异有效提升了跨域故障精度。 |
| 英文摘要: |
| Most of the existing unsupervised domain adaptive fault diagnosis methods are only implemented based on a single domain
signal, and the extracted fault information is not comprehensive enough. Only focus on realizing the edge distribution alignment of source
and target domain features, ignoring the conditional distribution differences of samples, which limits the improvement of diagnostic
accuracy. To overcome the above problems, a cross-domain fault diagnosis method of rolling bearings based on joint distribution offset
differences (JDOD) is proposed. Two structurally consistent CNNs are used to extract the time-domain and frequency-domain features of
the signal respectively to obtain more complete fault information. Joint distribution offset difference is proposed to realize edge distribution
alignment and conditional distribution alignment of different domain features. Comparing experiments with various advanced methods on
two multi-condition bearing datasets, the average diagnostic accuracy of more than 99% is obtained. The experimental results show that
the joint distribution offset difference effectively improves the cross-domain fault diagnostic accuracy. |
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