Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering

Abstract

Surface-mounted device (SMD) assembly machines refer to production lines that assemble a variety of products that fit their purposes. As the required products become more diverse, models that oversee product anomaly detection are also becoming increasing linearly. In order to efficiently oversee products, the number of models has to be reduced and products with similar characteristics have to be grouped and overseen. In this paper, we show that it is possible to handle a large number of new products using latent vectors obtained from the autoencoder model. By hierarchically clustering latent vectors, the model finds product groups with similar characteristics and oversees them by group. Furthermore, we validate our multi-product operation strategy for anomaly detection with a newly collected SMD dataset. Experimental results show that the anomaly detection method using hierarchical clustering of latent vectors is a practical management method for SMD anomaly detection.