Method of failure sign detection and factor estimation by graph neural network

Abstract

Predictive maintenance is a technique to conserve maintenance and it is the key effective maintenance operations and reduced downtime. Methods for predictive maintenance based on anomaly detection using deep learning have been actively studied, but the identification of anomalous sensors remains a challenging task. In this work, we use a graph neural network for anomaly detection and estimation. The vertices of the graph correspond to the sensors, so we can interpret the relevant weights as the relationship between the sensors. We specifically used sparse graph to improve graph interpretability and we confirmed the effectiveness of the method.