Unsupervised Anomaly detection of LM Guide Using Variational Autoencoder

Linear Motion (LM) is a linear motion guide that helps directional moving of machine. It is important to judge the anomaly state of LM guides because LM guides are used in various industries to support various task in industry application. In this paper, we proposed a machine learning algorithm for determining the anomaly state of LM guide. Considering that it is difficult to actually generate the anomaly signal, we trained model with only healthy state data. One of the generative models, variational autoencoder, is used for training healthy state data and the distribution of healthy state data is trained. Our trained model determines whether or not anomaly state has occurred based on a reconstruction error of the trained network.