Method, systems and apparatus for intelligently emulating factory control systems and simulating response data

Abstract

A simulated process is initiated. The simulated process includes generating, by an emulator, a control signal based on external inputs. The simulated process further includes processing, by a simulator, the control signal to generate simulated response data. The simulated process further includes generating, by a deep learning processor, expected behavioral pattern data based on the simulated response data. An actual process is initiated by initializing setpoints for a process station in a manufacturing system. The actual process includes generating, by the deep learning processor, actual behavioral pattern data based on actual process data from the at least one process station. The deep learning processor compares the expected behavioral pattern to the actual behavioral pattern. Based on the comparing, the deep learning processor determines that anomalous activity is present in the manufacturing system. Based on the anomalous activity being present, the deep learning processor initiates an alert protocol.