Digital twin application framework for complex equipment operation and maintenance system
A digital twin framework for complex equipment operation and maintenance systems,combined with deep learning technology,was proposed to address the urgent need for intelligent transformation in current complex equipment operation and maintenance systems.This article first established a digital twin theory and application framework for complex equipment operation and maintenance systems based on deep learning.At the theoretical level,a multi-level digital twin framework for operation and maintenance systems was constructed by combining deep learning.At the application level,a digital twin application framework for the entire life cycle of operation and maintenance system was constructed according to the parallel driving form of knowledge-data-model.From the data perspective,an application form combining the theoretical framework with the NST model was proposed and validated through experiments.The experimental results indicate that the NST model has better prediction performance for non-stationary time series data of high-speed trains.
digital twindeep learningcomplex equipmenttheoretical frameworkapplication frameworkentire life cycletime seriesintellectualization
万方数据
维普
