Real-time temperature prediction of lunar regolith drilling based on ATT-Bi-LSTM network

Abstract

Lunar drilling is conducted in extreme environments, where limited heat dissipation pathways may cause overheating during drilling, potentially leading to destructive effects on the drill tool. To address the challenge of rapid temperature change in lunar regolith drilling, this paper presents a spatiotemporal feature fusion method for the real-time prediction of drilling temperature inside a lunar regolith simulant. The method aims to accurately predict the temperature of eight positions on the drill tool in the next second based on seven state parameters during drilling from the previous 20 seconds. It utilizes deep learning techniques to combine the Bi-LSTM and self-attention mechanism to fully exploit past and future information to extract spatiotemporal features from time series data and focus on the crucial features for temperature prediction. Additionally, a proposed early stopping-Bayesian hyperparameter optimization method is incorporated to effectively adjust the hyperparameters of the model. Experimental results demonstrate the superior performance of this method on data from drilling experiments with lunar regolith simulant in a vacuum chamber, significantly outperforming other models in statistical terms and meeting real-time prediction requirements. Furthermore, it exhibits robustness and generalization capability in practical applications on two different robotic drills. This research provides reliable support for predicting drilling temperature in future lunar exploration missions and reveals the spatiotemporal relationship in the thermophysical field during drilling. It holds crucial significance in guiding mission planners and operators to proactively implement appropriate measures in advance, ensuring the smooth operation of the drilling apparatus and comprehending the generation, conduction, and dissipation mechanisms of heat during lunar regolith drilling.