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• W4285256586 abstract "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 F₁-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." @default.
• W4285256586 created "2022-07-14" @default.
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• W4285256586 date "2022-01-01" @default.
• W4285256586 modified "2023-09-30" @default.
• W4285256586 title "A Hybrid Machine-Learning Ensemble for Anomaly Detection in Real-Time Industry 4.0 Systems" @default.
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• W4285256586 doi "https://doi.org/10.1109/access.2022.3188102" @default.
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