Russian Federation
The article discusses the development and application of digital counterparts of cyberphysical systems (CFS) for monitoring and forecasting the condition of industrial facilities in real time. The main focus is on integrating neural network algorithms such as recurrent neural networks (RNN) and long-term short-term memory (LSTM) networks to dynamically predict changes in the state of a physical object based on data received through sensors. The proposed technique makes it possible to improve the accuracy of predictions and increase the adaptability of the system to rapidly changing conditions, which significantly increases the efficiency of industrial facilities.
hybrid architecture, digital twin, AI analytics, neural network algorithms, state prediction, LSTM (Long Short-Term Memory), CNN (Convolutional Neural Networks), adaptivity, monitoring, industrial facilities
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