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Machine Health Assessment Based on an Anomaly Indicator Using a Generative Adversarial Network
International Journal of Precision Engineering and Manufacturing ( IF 1.9 ) Pub Date : 2021-04-23 , DOI: 10.1007/s12541-021-00513-1
Hyung Jun Park , Seokgoo Kim , Seok-Youn Han , Seokju Ham , Kee Jun Park , Joo-Ho Choi

In prognostics and health management, the absence of fault data is a challenge that hinders practical applications in the field. When an absence occurs, the only option is to build a proper health indicator for anomaly detection. While there have been numerous traditional approaches toward this end, they have had drawbacks in one way or another. In this study, a new approach is proposed to develop an anomaly indicator that overcomes previous limitations by using a generative adversarial network (GAN). GANs have recently drawn attention as a means to generate virtual samples resembling the original distribution. Two examples—the bearing and train door system—are considered to examine the approach’s capabilities. The data acquired for the normal condition are used to train the GAN, the health is monitored over time using the trained GAN indicator, and the anomaly is successfully detected by identifying a decrease at a point in time.



中文翻译:

使用生成的对抗网络基于异常指标的机器健康评估

在预测和健康管理中,缺少故障数据是一个挑战,阻碍了该领域的实际应用。发生缺席时,唯一的选择是建立适当的健康指标以进行异常检测。尽管有许多传统的方法可以达到这个目的,但是它们以一种或另一种方式具有缺点。在这项研究中,提出了一种新方法来开发一种异常指示器,该异常指示器通过使用生成对抗网络(GAN)来克服以前的局限性。GAN最近作为一种生成类似于原始分布的虚拟样本的方法而引起了人们的关注。考虑了两个示例(轴承和火车门系统)来检查该方法的功能。正常情况下获取的数据用于训练GAN,使用训练后的GAN指示器随时间监测健康状况,

更新日期:2021-04-23
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