Anomaly detection of mechanical equipment with autoencoder

Abstract

We developed a system for detecting anomaly in mechanical equipment with neural network using sensor data. Mechanical equipment have many components and it is difficult to collect many anomaly data. In this research, we focused on the valve only and made an autoencoder that reconstructs only normal data with a neural network. We evaluated the autoencoder using data obtained from artificial trouble tests, where control parameters are changed based on the anomaly in the past. As a result, it was possible to discriminate the occurrence of anomaly.