A causal contrastive transformer model to predict grease pressure of shield machine tail

Abstract

Tunnel construction relies on large shield machines for excavation. During shield tunneling, the effective operation of the sealing system at the tail of the shield machine is crucial. To prevent sealing failure, maintaining the pressure balance inside and outside the sealing cavity is essential. Traditional methods indirectly assess the grease pressure inside the cavity by predicting grease consumption, but they are prone to errors and uncertainties. Nowadays, with advancements in sensor technology, it is possible to directly measure grease pressure, making direct prediction feasible. However, because the causal relationships between covariates such as shield tail attitude, slurry pressure, grease injection pressure and grease pressure are directional and sparse. Traditional time series forecasting techniques struggle to capture these causal relationships, which reduces the model performance. To address the above-mentioned issues, we propose a causal contrastive transformer(CCformer) model for the first time to directly predict the grease pressure. CCformer accurately models the causal relationships of covariates on the grease pressure through the dynamic influencer. Meanwhile, it makes good use of contrastive learning to gather the distributed similar causal relationships together, demonstrating a memory ability for them. The experimental results prove that CCformer performs better than other state-of-the-art models on the real shield tunneling construction dataset, with the average absolute error reduced by 6.6% and the mean squared error reduced by 5.6%. This research provides reliable support for ensuring the balance of pressure inside and outside the sealing cavity and contributes to improving construction safety.