This paper proposed an approach to estimate disc cutter wear utilizing a combination of multiple operational parameters and vibration data collected during shield tunneling operations. The incorporation of vibration signals, notably those originating from acceleration sensors mounted on the back plate of the soil chamber, has markedly enhanced the accuracy of the model. Time‐frequency domain features were extracted through analysis methods such as Fast Fourier Transform (FFT), Short‐Time Fourier Transform (STFT), and Continuous Wavelet Transform (CWT). A predictive model utilizing vibration and shield operation parameters was developed using the XGBoost algorithm, and a deep GoogLeNet Convolutional Neural Network (CNN) was trained on time‐frequency graphs from the CWT. In addition, this study also investigated the impact of signal duration on wavelet image information and model accuracy. In the Huang‐Shang Intercity Railway Project, the approach effectively assessed disc cutter wear during tunneling operations and dynamically optimized the operational parameters of the shield tunnel machine through predictive analysis.

中文翻译：

---

通过操作参数和振动数据识别滚刀磨损：案例研究


本文提出了一种利用多个操作参数和盾构隧道作业期间收集的振动数据的组合来估计滚刀磨损的方法。振动信号的加入，特别是来自安装在土壤室背板上的加速度传感器的振动信号，显著提高了模型的准确性。通过快速傅里叶变换 （FFT） 、短时傅里叶变换 （STFT） 和连续小波变换 （CWT） 等分析方法提取时频域特征。使用 XGBoost 算法开发了一个利用振动和保护罩操作参数的预测模型，并在 CWT 的时频图上训练了深度 GoogLeNet 卷积神经网络 （CNN）。此外，本研究还调查了信号持续时间对小波图像信息和模型精度的影响。在黄上城际铁路项目中，该方法有效地评估了隧道作业期间的滚刀磨损，并通过预测分析动态优化了盾构隧道机的运行参数。