Abstract
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.
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Acknowledgements
This work was supported by a Nation Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2019R1A2C2010028) and the R&D Program of the Korea Railroad Research Institute, Republic of Korea. The authors would like to thank Yun-Ho Seo of the Korea Institute of Machinery and Materials (KIMM) for conducting the bearing experiments and providing data.
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Hyung Jun Park and Seokgoo Kim are co-first authors.
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Park, H.J., Kim, S., Han, SY. et al. Machine Health Assessment Based on an Anomaly Indicator Using a Generative Adversarial Network. Int. J. Precis. Eng. Manuf. 22, 1113–1124 (2021). https://doi.org/10.1007/s12541-021-00513-1
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DOI: https://doi.org/10.1007/s12541-021-00513-1