Outlier detection in temporal spatial log data using autoencoder for industry 4.0

Abstract

Industry is changing rapidly under industry 4.0. The manufacturing process and its cyber-physical systems (CPSs) produce large amounts of data with many relationships and dependencies in the data. Outlier detection and problem solving is difficult in such an environment. We present an unsupervised outlier detection method to find outliers in temporal spatial log data without domain-specific knowledge. Our method is evaluated with real-world unlabeled CPS log data extracted from a quality glass inspection machine used in production. As a measurement metric for success, we set reasonable outlier areas in cooperation with a domain expert. Using our proposed method, we were able to find all known outlier areas. In addition, we found outliers that were not previously known and have been verified as outliers by a domain expert ex post.