一种用于盾构机刀具掘进过程的振动状态监测方法及系统

By employing a multi-source signal acquisition and multi-dimensional feature extraction method for monitoring the vibration status of tunnel boring machine (TBM) cutters, combined with sensor arrays and neural network models, the challenge of real-time assessment in TBM cutter monitoring has been solved. This method enables accurate identification of cutter status and risk warning, thereby improving construction efficiency and safety.

CN121456756BActive Publication Date: 2026-07-17NANJING UNIVERSE MASCH MOULD ITD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NANJING UNIVERSE MASCH MOULD ITD
Filing Date
2025-11-06
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing methods for monitoring tunnel boring machine (TBM) cutterheads are insufficient for real-time and accurate assessment of their working status and cannot effectively identify vibration patterns under different working conditions. This results in inadequate early warning capabilities for faults such as abnormal wear, uneven loading, and cutterhead breakage, affecting the continuity and economy of construction.

Method used

By employing multi-source tunneling vibration signal acquisition, multi-dimensional vibration feature extraction, correlation analysis, and hybrid neural network models, combined with sensor arrays and signal processing technology, a multi-dimensional vibration state identification and risk assessment system is constructed to achieve multi-dimensional real-time monitoring and intelligent identification of tunnel boring machine cutters.

Benefits of technology

It enables multi-dimensional real-time monitoring and intelligent identification of vibration status of tunnel boring machine cutters during excavation, accurately identifies normal cutting, light wear, severe wear, off-center operation, and cutter chipping, and triggers graded early warnings, thereby improving the continuity and economy of construction.

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Abstract

本发明提供一种用于盾构机刀具掘进过程的振动状态监测方法及系统，涉及盾构机工程技术领域，其方法包括采集盾构机刀具掘进过程中的多源掘进振动信号，通过布置于盾构机刀具的掘进振动传感器阵列获取；对多源掘进振动信号进行特征提取生成基于时频域的多维掘进振动特征；将多维掘进振动特征与掘进参数进行关联分析形成掘进振动工况判断标准；基于多维掘进振动特征和掘进振动工况判断标准构建混合掘进振动神经网络模型，对盾构机刀具的掘进振动状态进行识别输出掘进振动状态类别；建立掘进振动风险评估指标体系生成掘进振动风险等级，触发掘进振动风险等级的分级预警机制，从而可以对盾构机刀具掘进过程中振动状态进行多维度实时监测和智能识别。
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Citation Information

Patent Citations

  • Load self-sensing tunneling cutter and cutter monitoring method

    CN119531890A

  • Shield tunneling machine cutterhead center area deformation detection system and method

    CN119984165A