RCLSTMNet: A Residual-convolutional-LSTM Neural Network for Forecasting Cutterhead Torque in Shield Machine
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12555-022-0104-x.pdf
Reference43 articles.
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2. C. Qin, R. Wu, G. Huang, J. Tao, and C. Liu, “A novel LSTM-autoencoder and enhanced transformer-based detection method for shield machine cutterhead clogging,” Science China Technological Sciences, vol. 66, pp. 512–527, 2023.
3. C. Qin, G. Shi, J. Tao, H. Yu, Y. Jin, J. Lei, and C. Liu, “Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network,” Mechanical Systems and Signal Processing, vol. 151, 107386, April 2021.
4. Y. Jin, C. Qin, J. Tao, and C. Liu, “An accurate and adaptative cutterhead torque prediction method for shield tunneling machines via adaptative residual long-short term memory network,” Mechanical Systems and Signal Processing, vol. 165, 108312, February 2022.
5. H. Yu, J. Tao, S. Huang, C. Qin, D. Xiao, and C. Liu, “A field parameters-based method for real-time wear estimation of disc cutter on TBM cutterhead,” Automation in Construction, vol. 124, 103603, April 2021.
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