A Prediction Technique Based on Deep Learning for Deformation and Missing Data in the Context of Insufficient Sensor Data

There is some interpretability in predicting deformation based on key control points [1], but mispredicting the deformation of the test model can result from missing sensor data due to physical obstruction and going outside the equipment's measuring range. In order to address the issue, this paper combines key control points to forecast the deformation in real time, introduces K-neighborhoods (KNN) to obtain the spatial topological relationship of the tested model, and integrates hybrid block-attention mechanism (CBAM) and bi-directional long and short-term memory cells (BiLSTM) to improve the linkage between multiple input features. The network has a faster convergence rate, according to experiments, and its maximum deformation prediction deviation is only 0.28 mm. In the meantime, the prediction approach uses standardization to make up for the missing control point data; this results in a corrective impact that ranges from 37.76% to 90.89%. The method proposed in this paper complements the missing sensor data while predicting the deformation in real time, which is essential for establishing an accurate dynamic prediction model and realizing comprehensive data sensing.