余碧桢

个人信息

参与实验室科研项目

人机协同柔性智造关键技术与集成验证

掘进机盾尾密封及刀具状态智能诊断与评价技术研究

奖励荣誉

中国科学技术大学2024年度“雏鹰基金”原创课题负责人

学术成果

共撰写/参与撰写专利 4 项，录用/发表论文 1 篇，投出待录用论文1篇。

patent

1. 基于因果对比模型的盾构机尾部油脂压力预测方法及系统 赵云波, 余碧桢, 谭娜, 刘斌琨, 魏晓龙, and 许镇义 2025 [Abs] [pdf]

   本发明公开了基于因果对比模型的盾构机尾部油脂压力预测方法及系统，涉及盾构机技术领域，将盾尾姿态参数输入到已训练完成的因果对比模型中，输出油脂压力预测值；因果对比模型的训练过程如下：将盾尾姿态参数和油脂压力拼接并分割，将每个片段转化为高维特征向量；将高维特征向量输入到定向影响器，得到因果关系特征；将油脂压力输入到趋势提取器中，通过多层感知机独立提取油脂压力历史数据的趋势特征，输出压力趋势特征；基于因果对比损失以及平均绝对误差损失构建总损失函数，以对因果对比模型中的可训练参数进行调整；该盾构机尾部油脂压力预测方法及系统，更准确的预测油脂压力。

2. 盾尾腔状态预测方法及装置 谭娜, 余碧桢, 荆留杰, 赵云波, 魏晓龙, 许镇义, 焦敬波, and 王震 2024 [Abs] [pdf]

   本发明公开了一种盾尾腔状态预测方法及装置，其中该方法包括：获取盾尾腔的内部传感器数据和外部传感器数据；将外部传感器数据输入外部特征提取模型，输出外部特征数据；所述外部特征提取模型是利用历史的外部传感器数据，对全连接自编码器训练得到；将内部传感器数据输入腔内压力特征提取模型，输出内部特征数据；所述腔内压力特征提取模型包括多个类型的堆栈块，每个堆栈块包括两个基块，两个基块多次循环进行腔内压力的反向预测和前向预测；所述类型包括趋势性预测和周期性预测；采用均方误差损失函数，将外部特征数据和内部特征数据进行融合，输出腔内压力的预测数据。本发明可以提高盾尾腔内压力的预测精度，提前识别潜在风险。

3. 液压减震器无监督故障检测方法、系统、设备与存储介质 赵云波, 刘斌琨, 康宇, 曹洋, 余碧桢, and 陈龙鑫 2024 [Abs] [pdf]

   本发明公开了一种液压减震器无监督故障检测方法、系统、设备与存储介质，它们是一一对应的方案，方案中：利用减震器健康状态下的数据无监督的训练检测模型，具体的，对比利用健康中心和健康样本间的来度量样本一致性，对比健康中心多传感器信号相关性和健康样本的多传感器信号相关性来度量多传感器模式一致性，继而通过两类一致性直接建模减震器的工作状态，可以在没有故障样本参与训练的前提下仍然实现较好的检测结果。

4. 一种基于因果对比模型的盾构机尾部油脂压力预测的方法 赵云波, 余碧桢, 谭娜, 刘斌琨, 魏晓龙, and 许镇义

Journal Articles

1. A Causal Contrastive Transformer Model to Predict Grease Pressure of Shield Machine Tail Bizhen Yu, Na Tan, Yunbo Zhao, Xiaolong Wei, Zhen Wang, Binkun Liu, Jun Huang, and Zhenyi Xu Intelligent Data Analysis: An International Journal 2026 [Abs] [doi] [pdf]

   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.

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