Kim Myungyon, Ko Jin Uk, Lee Jinwook, Youn Byeng D, Jung Joon Ha, Sun Kyung Ho
Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul 08826, Republic of Korea.
Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul 08826, Republic of Korea; OnePredict Inc., Seoul 06160, Republic of Korea; Institute of Advanced Machines and Design, Seoul National University, Seoul 08826, Republic of Korea.
ISA Trans. 2022 Jan;120:372-382. doi: 10.1016/j.isatra.2021.03.002. Epub 2021 Mar 6.
Recently, substantial research has explored the development of deep-learning-based methods to diagnose faults in rotating machinery. For these diagnosis methods, it is difficult to obtain high target diagnosis accuracy when the amount of labeled data obtained pertaining to the rotating machinery under study is insufficient or in cases involving a discrepancy in the distribution types found in the training and test data sets. To deal with this research need, the paper outlines a new method, a domain adaptation with semantic clustering (DASC), capable of diagnosing faults in rotating machinery. The method outlined in this research learns both domain-invariant and discriminative features. The method reduces the domain discrepancy by minimizing the domain-related loss. In addition, by defining an additional loss, which is called semantic clustering loss, and reducing it at multiple feature levels, the DASC method learns features that make samples better semantically clustered, according to their health conditions. Consequently, fault diagnosis performance for target rotating machinery can be enhanced through the use of the DASC approach. The effectiveness of the DASC approach is confirmed by examining various fault diagnosis situations with domain discrepancies across the source and target domains, using experimental data from three bearing systems. Also, various analyses are explored to better understand the advantages of the DASC method.
最近，大量研究探索了基于深度学习的方法在旋转机械故障诊断中的发展。对于这些诊断方法，当所获得的与正在研究的旋转机械相关的标记数据量不足时，或者在训练和测试数据集中发现分布类型存在差异的情况下，很难获得较高的目标诊断准确率。为了满足这一研究需求，本文概述了一种新方法，即语义聚类域自适应（DASC），它能够诊断旋转机械中的故障。本研究中概述的方法学习域不变和判别性特征。该方法通过最小化与域相关的损失来减少域差异。此外，通过定义一种称为语义聚类损失的附加损失，并在多个特征级别上减少它，DASC方法学习能够根据样本的健康状况使样本在语义上更好地聚类的特征。因此，通过使用DASC方法可以提高目标旋转机械的故障诊断性能。通过使用来自三个轴承系统的实验数据，研究了源域和目标域之间存在域差异的各种故障诊断情况，从而证实了DASC方法的有效性。此外，还进行了各种分析，以更好地理解DASC方法的优势。
