多源域联合对齐的自适应故障诊断方法
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TP391.9; TP206

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山西省科技重大专项计划“揭榜挂帅”项目(202201090301013);山西省基础研究计划(自由探索类)面上项目(202203021221142);太原科技大学博士启动金(20222131)


Fault Diagnosis Method Based on Joint Alignment of Multiple Source Domain Adaptation
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    摘要:

    单源域自适应故障诊断方法常出现域不匹配的问题,导致负迁移和泛化能力不足。同时,实际工业中往往包含多个源域数据,且目标域中包含的信息在不同源域中存在较大差异。因此,提出一种多源域联合对齐的自适应故障诊断方法。首先,面对多传感信号,采用平均拼接融合方法,形成融合信号;其次,提出嵌入可迁移残差模块的多尺度特征提取模块,既保证多尺度的特征提取,又增强模型的非额外参数化可迁移性。最后,结合自适应超参数和多核最大均值差异作为正则项减少网络层中数据分布的差异。将可迁移残差模块作为结构优化策略和多核最大均值差异作为统计变换策略联合使用,称之为联合对齐。实验结果可以看出,整个模型无需引入多余的超参数,即可实现多源域的高准确率故障诊断需求。

    Abstract:

    Single-source domain adaptation fault diagnosis methods often suffer from domain mismatch problems, resulting in negative transfer and insufficient generalization capabilities. At the same time, actual industry often contains data from multiple source domains, and the information contained in the target domain varies greatly in different source domains. Therefore, the fault diagnosis method based on joint alignment of multiple source domain adaptation is proposed. First, in the face of multi-sensor signals, the average splicing fusion method is used to form the fusion signal. Second, the multi-scale feature extraction module with transferable residual module is proposed to ensure multi-scale feature extraction and enhance the non-extra parameterized transferability of the model. Finally, adaptive hyperparameters and multi-kernel maximum mean discrepancies are combined as constraints to eliminate the differences in data distribution in the network layer. The transferable residual module as a structural optimization strategy and multi-kernel maximum mean discrepancies as a statistical transformation strategy are jointly applied, which is called joint alignment. Experimental verification shows that the entire model can achieve high-accuracy fault diagnosis requirements in multi-source domains without introducing redundant hyperparameters.

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聂晓音,韩秦,吴沛澜,等. 多源域联合对齐的自适应故障诊断方法[J]. 科学技术与工程, 2024, 24(28): 12127-12134.
Nie Xiao-yin, Han Qin, Wu Pei-lan, et al. Fault Diagnosis Method Based on Joint Alignment of Multiple Source Domain Adaptation[J]. Science Technology and Engineering,2024,24(28):12127-12134.

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  • 收稿日期:2024-01-13
  • 最后修改日期:2024-08-04
  • 录用日期:2024-04-25
  • 在线发布日期: 2024-11-05
  • 出版日期: