Fully Unsupervised Defect Clustering using Adversarial Autoencoder



Published Sep 4, 2023
Taewan Kim Seungchul Lee


Identifying defect types and developing proper maintenance strategies is a major concern in modern industry. Most conventional studies have been conducted primarily based on a supervised learning scheme. However, supervised learning has a critical limitation in that it requires labeled data, which is difficult and expensive to obtain in real-world industry. Considering that there are many industries that do not perform post investigations on the defects, fully unsupervised learning methods, which do not exploit any information such as label data or the number of types, need to be developed. Accordingly, in this study, we propose a fully unsupervised defect clustering method that does not exploit any information other than the data itself. The proposed method consists of two major components. The first is dimensionality reduction into latent space via adversarial autoencoder, and the second is a Bayesian mixture model for distribution estimation in latent space. The experiments on a rolling-element-bearing dataset validate the effectiveness of our method. Specifically, our method performs defect clustering without any information other than the data itself, which is promising for real industrial applications.

Abstract 89 | PDF Downloads 111



Deep Learning, Unsupervised Learning, Adversarial Autoencoder, Bayesian Mixture Model

Hoang, D.-T. & Kang, H.-J. (2019). A survey on deep learning based bearing fault diagnosis. Neurocomputing, vol. 335, pp. 327-335.

Duan, Z. Wu, T. Guo, S. Shao, T. Malekian, R. & Li, Z. (2018). Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review. The International Journal of Advanced Manufacturing Technology, vol. 96, no. 1, pp. 803-819.

Liu, R. Meng, G. Yang, B. Sun, C. & Chen, X. (2016). Dislocated time series convolutional neural architecture: An intelligent fault diagnosis approach for electric machine, IEEE Transactions on Industrial Informatics, vol. 13, no. 3, pp. 1310-1320.

Liu, H., Zhou, J., Zheng, Y., Jiang, W., & Zhang, Y. (2018). Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA transactions, 77, 167178.

Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2015). Adversarial autoencoders. arXiv preprint arXiv:1511.05644.

Kim, T., & Lee, S. (2022). A Novel Unsupervised Clustering and Domain Adaptation Framework for Rotating Machinery Fault Diagnosis. IEEE Transactions on Industrial Informatics. Case Western Reserve University Bearing Data Center, [Online]. Available: https://csegroups.case.edu/bearingdatacenter/home

Li, X., Jia, X. D., Zhang, W., Ma, H., Luo, Z., & Li, X. (2020). Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation. Neurocomputing, 383, 235-247.
Regular Session Papers