A Novel Reconstruction Method for Irregularly Sampled Observation Sequences for Digital Twin - Haonan Jiang; Yanbo Zhao; Qiao Zhu; Yuanli Cai (Applied Sciences)
Various uncertainties such as communication delay, packet loss and disconnection in the Industrial Internet, as well as the asynchronous sampling of sensors, can cause irregularity, sparsity, and misalignment of sampling sequences, and thereby seriously affect the training and prediction performance of a digital twin model. Sequence reconstruction is an effective way to deal with the above problems, but if the measurement data become sparse or contain significant noise due to packet loss and electromagnetic interference, existing methods struggle to achieve ideal results. Therefore, a novel variational autoencoder model based on a parallel reference network and neural controlled differential equation (PRN-NCDE) is proposed in this article to solve the problem of reconstructing irregular series under sparse measurements and high noise levels. First, a multi-channel self-attention module is established, which can not only analyze the position and feature information of the sampled data to improve the reconstruction accuracy under sparse measurements, but also effectively tackle the misalignment and irregularity of the observation sequence through multi-channel and mask mechanisms. Second, to improve the accuracy of sequence reconstruction under large noise levels, a PRN is established to obtain reference features, which are weighted and fused with the features of observed data. Third, we use the NCDE to construct a decoder that can combine the control input of the system to predict the output values to solve the problem of sequence reconstruction in a controlled system. Finally, a weighted loss function is constructed to better train the network parameters of the model. This article takes the furnace of the boiler system in a coal-fired power plant as the test object to verify the effectiveness and fitting accuracy of the proposed PRN-NCDE model compared to the existing methods for a controlled system under sparse measurements and large noise levels. Simulation results show that the proposed PRN-NCDE model can improve the estimation accuracy by more than 50% and 70% compared with the recurrent neural network-NCDE (RNN-NCDE) under different sampling numbers and noise levels, and by more than 80% and 60% compared with the recurrent neural network-NODE (RNN-NODE).