Feng Yong, Chen Jinglong, Zhang Tianci, He Shuilong, Xu Enyong, Zhou Zitong
State Key Laboratory for Manufacturing and Systems, Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
State Key Laboratory for Manufacturing and Systems, Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
ISA Trans. 2022 Jan;120:383-401. doi: 10.1016/j.isatra.2021.03.013. Epub 2021 Mar 16.
In the engineering practice, lacking of data especially labeled data typically hinders the wide application of deep learning in mechanical fault diagnosis. However, collecting and labeling data is often expensive and time-consuming. To address this problem, a kind of semi-supervised meta-learning networks (SSMN) with squeeze-and-excitation attention is proposed for few-shot fault diagnosis in this paper. SSMN consists of a parameterized encoder, a non-parameterized prototype refinement process and a distance function. Based on attention mechanism, the encoder is able to extract distinct features to generate prototypes and enhance the identification accuracy. With semi-supervised few-shot learning, SSMN utilizes unlabeled data to refine original prototypes for better fault recognition. A combinatorial learning optimizer is designed to optimize SSMN efficiently. The effectiveness of the proposed method is demonstrated through three bearing vibration datasets and the results indicate the outstanding adaptability in different situations. Comparison with other approaches is also made under the same setup and the experimental results prove the superiority of the proposed method for few-shot fault diagnosis.
在工程实践中，缺乏数据尤其是带标签的数据通常会阻碍深度学习在机械故障诊断中的广泛应用。然而，收集和标注数据往往既昂贵又耗时。为了解决这个问题，本文提出了一种具有挤压激励注意力机制的半监督元学习网络（SSMN）用于少样本故障诊断。SSMN由一个参数化编码器、一个非参数化原型细化过程和一个距离函数组成。基于注意力机制，编码器能够提取独特特征以生成原型并提高识别精度。通过半监督少样本学习，SSMN利用未标注数据来细化原始原型以实现更好的故障识别。设计了一种组合学习优化器来高效地优化SSMN。通过三个轴承振动数据集验证了所提方法的有效性，结果表明其在不同情况下具有出色的适应性。在相同设置下与其他方法进行了比较，实验结果证明了所提方法在少样本故障诊断方面的优越性。
