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Detecting faults and anomalies in real-time industrial systems is a challenge due to the difficulty of sufficiently covering an industrial system&#x2019;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&#x2013;Local Outlier Factor, One-Class Support Vector Machine, and Autoencoder&#x2013;, through a weighted average to improve anomaly detection. The ensemble model was tested with three air-blowing machines obtaining a <inline-formula> <tex-math notation="LaTeX">${F}_{{1}}$ </tex-math></inline-formula>-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.

A Hybrid Machine-Learning Ensemble for Anomaly Detection in Real-Time Industry 4.0 Systems