CPS is potential application in various fields, such as medical, healthcare, energy, transportation, and defense, as well as Industry 4.0\nin Germany. Although studies on the equipment aging and prediction of problemhave been done by combining CPSwith Industry\n4.0, such studies were based on small numbers and majority of the papers focused primarily on CPS methodology. Therefore, it is\nnecessary to study active self-protection to enable self-management functions, such as self-healing by applying CPS in shop-floor.\nIn this paper, we have proposedmodeling of shop-floor and a dynamic reconfigurable CPS scheme that can predict the occurrence\nof anomalies and self-protection in the model. For this purpose, SVMwas used as a machine learning technology and it was possible\nto restrain overloading in manufacturing process. In addition, we design CPS framework based on machine learning for Industry\n4.0, simulate it, and perform. Simulation results show the simulation model autonomously detects the abnormal situation and it is\ndynamically reconfigured through self-healing.
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