Introducing AnomDB: An Unsupervised Anomaly Detection Method for CNC Machine Control Data

Abstract

We propose the application of unsupervised machine learning to automatically detect anomalous behavior preceding machine failure. We achieve this through an approach that utilizes Principal Component Analysis (PCA), latent feature extraction, and Density Based Scanning of Applications with Noise (DBSCAN). We call this method AnomDB. Time series data collected from Computer Numerically Controlled (CNC) machines may benefit from this technique due to its ability to consolidate noisy, multivariate data from CNC controls and detect anomalies without reliance on periodicity of signal. We perform experiments on CNC machine control data to demonstrate the effectiveness of this method in discovering anomalies over other commonly used methods of anomaly detection such as IQR and k-means clustering. We show the effectiveness of this method on a representative example in an actual machine shop, and then on a series of real machining data with synthetic anomalies injected.