Automated Data Labeling and Anomaly Detection Using Airborne Sound Analysis

Abstract

Anomaly detection is facing the challenge of generating a maximum of information with a limited number of sensors in production machines and minimal effort of data analysis. Machine operators are often able to perceive changes acoustically and based on experience. In order to imitate this intelligent human capability, a systematic methodology has been developed in this work. Firstly, an introduction to this topic will be given and the acoustic sensor set up as well as the data preprocessing will be described. Secondly, an approach for automatic data labeling as an input for the intelligent anomaly detection will be presented. Using a model-based approach, anomaly detection is performed in the next step. This allows not only the detection of gradual and sudden process changes but also systematic problem solving. Finally, the anomaly detection will be validated for cutting processes.