Fully Unsupervised Defect Clustering using Adversarial Autoencoder

Abstract

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.