Identification of Disc Cutter Wear via Operation Parameters Combined With Vibration Data: A Case Study

• Published in: International journal for numerical and analytical methods in geomechanics Vol. 49; no. 1; pp. 256 - 279
• Main Authors: Wang, Yan‐Ning, Chen, Han, Min, Xin‐Hao, Zhao, Lin‐Shuang
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.01.2025
• Subjects: Accuracy; Algorithms; Artificial neural networks; Continuous wavelet transform; cutter life; cutter wear; deep learning; Disc cutters; Fast Fourier transformations; Fourier analysis; Fourier transforms; Graphs; Image processing; Impact analysis; Neural networks; Parameter estimation; Parameter identification; Parameters; prediction model; Prediction models; Tunneling; Tunneling shields; Vibration; Vibration analysis; vibration signal; Wavelet transforms; Wear
• ISSN: 0363-9061; 1096-9853
• DOI: 10.1002/nag.3872

ABSTRACT 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.