Unsupervised Anomaly Detection and Root Cause Analysis for an Industrial Press Machine based on Skip-Connected Autoencoder

Abstract

We propose an unsupervised-learning-based method for anomaly detection and root cause analysis for an industrial press machine. A skip-connected autoencoder with 55% performance improvement measured by reconstruction root mean square error to vanilla variant in average is used to train the collected multivariant time series data in different schemes. We then conduct a stacked evaluation method for both machine- level anomalies with the root cause localization and anomaly on specific cylinder tracks. Both real-world and synthetic anomalies embedded in real data are used for evaluation. The result shows that the multi-models training scheme and the relatively short window length can gain better performance, i.e., fewer anomaly false alarms and misses.