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Precise Cutterhead Clogging Detection for Shield Tunneling Machine Based on Deep Residual Networks

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  • Intelligent Control and Applications
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Abstract

During the construction process of tunnels, the cutterhead of shield tunneling machines may get clogged due to clay adhesion, which may seriously affect the efficiency of the project. Therefore, finding an intelligent diagnosis method to detect the clogging status is of great importance. In this study, a deep residual network-based method for diagnosing cutterhead clogging on shield tunneling machines is proposed. First, working state data of the shield tunneling machine is screened out, and parameters reflecting the clogging state are selected for further analysis. After eliminating extreme outliers, an empirical formula is proposed to label the data. At the same time, several time-domain features of the selected excavation parameters within every five minutes are extracted. These features are then fed into the proposed model as the input data to realize clogging detection. Because the original dataset is unbalanced, the combination of f1-score and accuracy is used to evaluate the performance of the proposed model. The results show that the accuracy of the proposed algorithm reaches 95.71%, which is 1.21%, 2.84%, 9.84%, 6.04%, and 0.86% higher than the support vector machine-based, random forest-based, AdaBoost-based, extreme gradient boosting-based and deep neural network-based methods. The f1 score of the proposed model is 0.923, which is also 0.038, 0.042, 0.269, 0.169 and 0.02 higher than those compared methods. Therefore, the proposed deep residual network-based method can accurately detect cutterhead clogging conditions.

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Authors and Affiliations

  1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Ruihong Wu, Chengjin Qin, Guoqiang Huang, Jianfeng Tao & Chengliang Liu

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  1. Ruihong Wu
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  2. Chengjin Qin
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Correspondence to Chengjin Qin.

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The authors declare that there is no competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.

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This work was partially supported by the National Natural Science Foundation of China (52375255) and Shanghai Municipal Science and Technology Major Project (Grant No. 2021SHZDZX0102).

Ruihong Wu received his B.S. degree in mechanical engineering from Tongji University, Shanghai, China in 2021. He is currently a M.S. student in mechanical engineering at Shanghai Jiao Tong University. His research interests include signal processing and deep learning-based intelligent PHM.

Chengjin Qin received his Ph.D. degree from Shanghai Jiao Tong University, Shanghai, China, in 2018. He is currently an associate professor in mechanical engineering at Shanghai Jiao Tong University. His research interests include signal processing, deep learning, PHM, and dynamics.

Guoqiang Huang received his B.S. degree in mechanical engineering from Chongqing University, Chongqing, China in 2021. He is currently a M.S. student in mechanical engineering at Shanghai Jiao Tong University. His research interests include time-frequency analysis, signal decomposition, deep learning, and PHM.

Jianfeng Tao received his Ph.D. degree from the School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics in 2003. He was promoted to professor in 2022 at the Shanghai Jiao Tong University. His research interests are in intelligent sensing and control of complex mechatronics systems, with particular attention to robotics system, construction machinery, and industrial fluid power transmission and control system.

Chengliang Liu received his Ph.D. degree in mechanical engineering from Southeast University, China, in 1999. He was promoted to Full Professor in 2002 at Shanghai Jiao Tong University. His research interests include intelligent robot systems, power electronics, network-based monitoring, and PHM.

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Wu, R., Qin, C., Huang, G. et al. Precise Cutterhead Clogging Detection for Shield Tunneling Machine Based on Deep Residual Networks. Int. J. Control Autom. Syst. 22, 1090–1104 (2024). https://doi.org/10.1007/s12555-022-0576-8

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  • DOI: https://doi.org/10.1007/s12555-022-0576-8

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