Intelligent anomaly detection of machine tools based on machine learning methods

Applications for artificial intelligence in production science are on the brink of industrial implementation. For an anomaly detection in machine tools, fixed threshold limits in signal data are necessary. To develop an autonomous anomaly detection without fixed limits, recurring segments have to be detected. In this article a procedure for online pattern recognition in NC code is described, where the recognized segments will be matched with drive signals. The intelligent system independently learns individual threshold limits. This enables an anomaly detection in online drive signals. The user can classify these faults and receives recommendations for action. © 2019 Carl Hanser Verlag. All rights reserved.