A Hybrid Machine-Learning Ensemble for Anomaly Detection in Real-Time Industry 4.0 Systems
David Velásquez, E. Pérez, Xabier Oregui, Arkaitz Artetxe, Jorge Manteca, Jordi Escayola Mansilla, Mauricio Toro, Mikel Maiza, Basilio Sierra
Abstract
Detecting faults and anomalies in real-time industrial systems is a challenge due to the difficulty of sufficiently covering an industrial system’s complexity. Today, Industry 4.0 makes it possible to tackle these problems through emerging technologies such as the Internet of Things and Machine Learning. This paper proposes a hybrid machine-learning ensemble real-time anomaly-detection pipeline that combines three Machine Learning models –Local Outlier Factor, One-Class Support Vector Machine, and Autoencoder–, through a weighted average to improve anomaly detection. The ensemble model was tested with three air-blowing machines obtaining a F<sub>1</sub>-score value of 0.904, 0.890, and 0.887, respectively. The results of the ensemble model showed improved performance metrics concerning the individual metrics. A novelty of this model is that it consists of two stages inspired by a standard industrial system: i) a manufacturing stage and ii) an operation stage.