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Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning
IEEE Industrial Electronics Magazine  (IF6.625),  Pub Date : 2021-01-18, DOI: 10.1109/mie.2020.3034884
Benjamin Maschler, Michael Weyrich

Deep learning has greatly increased the capabilities of "intelligent" technical systems over the last years [1]. This includes the industrial automation sector [1]-[4], where new data-driven approaches to, for example, predictive maintenance [2], computer vision [3], or anomaly detection [4], have resulted in systems more easily and robustly automated than ever before.