Feature Extraction and Anomaly Detection by Non-negative Matrix Factorization of Vibration Data of Machine

Abstract

In recent years, in the field of manufacturing industry, there is an increasing need for anomaly detection by collection and analysis of sensor data for predictive maintenance of factory equipment and industrial product.In the conventional method, for example, attention is focused on the power of a specific frequency band related to the anomaly of the equipment, and anomaly detection is performed by comparing the magnitude of the power.However, when there are many or wide frequency bands involved in anomaly, they may not be clearly specified. Therefore, we propose a method for anomaly detection using non-negative matrix factorization which is an unsupervised learning algorithm. Even if the frequency band of the vibration data is not clearly specified, our proposed method can automatically extract the features of vibration data and detect anomaly of machines accurately.