无刷直流电机角域数据混合域泛化故障诊断
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中国航空工业集团公司成都飞机设计研究所 成都 610041

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TM30;TN710

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科工局项目(JCKY2022205A003)资助


Angular domain data mixed domain generalization for fault diagnosis of brushless DC motor
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AVIC Chengdu Aircraft Design & Research Institute,Chendu 610041, China

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    摘要:

    无刷直流电机(BLDC)在众多工业领域被广泛应用,BLDC在重要的应用场景中发生故障容易造成重大经济损失甚至人员伤亡,因此对其开展故障诊断研究具有重要意义。BLDC常处于变工况服役环境中,用于数据驱动模型训练的源域和目标域通常具有分布差异,现有众多学者利用域自适应迁移学习方法解决该问题。但是域自适方法需要在训练过程中访问目标域,这给模型的部署应用带来了不便。因此本文提出了角域数据混合域泛化网络解决此问题,所提方法能够利用BLDC不同工况的多个源域进行学习,挖掘域泛化知识,从而在未见过的目标域上具有较好泛化性能,具备一次训练,多应用场景部署能力。所提方法利用角域电流重采样方法将BLDC的时域电流转换为角域电流以缓解工况影响,基于卷积神经网络架构模型搭建故障诊断网络,并利用先进的数据增强方法Mixup对训练数据进行处理,改善模型泛化性能。最后基于丰富的BLDC故障实验数据将所提方法与其他先进方法相比,对比结果证明所提方法具备优异的域泛化故诊断性能。

    Abstract:

    Brushless dc motors (BLDCs) are widely used in various industrial fields. Failures in critical applications can lead to significant economic losses or even casualties, highlighting the importance of fault diagnosis research. BLDCs often operate under varying conditions, leading to the differences between the source and target domains used in data-driven models. While domain adaptation methods are commonly used to address this problem, they require access to the target domain during training, complicating model deployment. To overcome this, we propose the angular domain data mixed domain generalization network (ADMDG). This method leverages multiple source domains from different BLDC operating conditions to learn domain-generalized knowledge, enabling effective generalization to unseen target domains and allowing for a single training process to support multiple deployment scenarios. ADMDG employs an angular domain current resampling technique to convert timedomain currents into angular-domain currents, mitigating the impact of varying conditions. A convolutional neural network-based fault diagnosis model is constructed, and advanced data augmentation techniques, Mixup, are used to enhance model generalization. Extensive BLDC fault experiments demonstrate the superior domain generalization performance of the proposed method compared to other state-of-the-art approaches.

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陈银超,王涛,王凯,梁兆鑫,王睿.无刷直流电机角域数据混合域泛化故障诊断[J].电子测量技术,2025,48(9):94-99

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  • 在线发布日期: 2025-05-23
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