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Shield cutterhead torque multi-step prediction method and system

A multi-step prediction, shield cutter head technology, applied in neural learning methods, earthwork drilling, neural architecture, etc., can solve problems such as difficulty in accurately predicting geological conditions, and achieve improved automation and intelligence, efficient and safe advancement Effect

Active Publication Date: 2021-08-06
SHANGHAI JIAO TONG UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, it is currently difficult to accurately predict geological conditions prior to excavation

Method used

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  • Shield cutterhead torque multi-step prediction method and system
  • Shield cutterhead torque multi-step prediction method and system
  • Shield cutterhead torque multi-step prediction method and system

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Experimental program
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Embodiment 1

[0054] Aiming at the problems of low prediction accuracy and weak generalization ability existing in the current cutterhead torque prediction method, the present invention provides a multi-step prediction method for shield cutterhead torque based on VMD-EWT-LSTM.

[0055]According to a kind of VMD-EWT-LSTM-based shield cutter head torque multi-step prediction method provided by the present invention, comprising:

[0056] Step 1: The cutterhead torque signal of the actual working process of the shield machine and preprocessing; Step 2: Use VMD to decompose the cutterhead torque subsequence into several subsequences and residual sequences, and use EWT to further decompose the residual sequence; Step 3: Normalize the decomposed torque subsequence, and send it to the neural network model for the next step; Step 4: Use the LSTM neural network and use the keras package under the Tensorflow framework to construct the VMD-EWT-LSTM shield cutterhead torque multiplier The multi-step pre...

Embodiment 2

[0086] Embodiment 2 is a modified example of Embodiment 1.

[0087] refer to Figure 1 to Figure 3 , the present invention provides a method for real-time prediction of cutter head torque based on residual CNN-LSTM neural network, comprising the following steps:

[0088] Step 1: Select the cutter head torque signal of the actual working process of the shield machine and perform preprocessing, and use the shield machine operating parameter data at the first 10 historical moments to predict the cutter head torque value at the next 5 moments;

[0089] Step 2: Use VMD to decompose the cutterhead torque subsequence into several subsequences and residual sequences, and use EWT to further decompose the residual sequence;

[0090] Step 3: Use the maximum-minimum normalization method to normalize the decomposed torque subsequence, and send it to the neural network model in the next step;

[0091] Step 4: Use the LSTM neural network and use the keras package under the Tensorflow frame...

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Abstract

The invention provides a shield cutterhead torque multi-step prediction method and system. The shield cutterhead torque multi-step prediction method comprises the steps: collecting and preprocessing cutterhead torque signals into a cutterhead torque sequence; decomposing the cutterhead torque sequence into a plurality of subsequences and a residual sequence by using a VMD decomposition method, and further decomposing the residual sequence by using an EWT decomposition method; normalizing the torque subsequences and transmitting the normalized torque subsequences to an LSTM neural network; constructing and training a shield cutter torque neural network multi-step prediction model; predicting a cutterhead torque value at a preset future moment; and respectively calculating a root-mean-square error, a mean absolute error and a mean absolute percentage error according to the cutterhead torque value at the preset future moment, and testing the prediction precision of the cutterhead torque. High-precision real-time multi-step prediction of the cutterhead torque is achieved, a driver can be guided to adjust operation parameters of the shield tunneling machine in advance, efficient and safe propulsion of the shield tunneling machine is achieved, and therefore the automation level and the intelligent level of the shield tunneling machine are improved.

Description

technical field [0001] The invention relates to the technical field of parameter prediction and optimization, in particular to a multi-step prediction method and system for shield cutter head torque. In particular, it relates to a multi-step prediction method of shield cutterhead torque based on variational mode decomposition-empirical wavelet transform-long short-term memory network (VMD-EWT-LSTM). Background technique [0002] In the field of tunnel construction, shield construction is safer, cleaner and more efficient than traditional blasting methods. Therefore, the shield machine is widely used in water conservancy, road and railway tunnel construction. In order to ensure the safe construction of the shield machine, the operating parameters of the equipment must be adjusted according to the geological environment. However, it is currently difficult to accurately predict the geological conditions before excavation. Compared with the prediction of geological conditions...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06F30/17E21D9/08G06N3/04G06N3/08
CPCG06F30/27G06F30/17E21D9/08G06N3/08G06N3/044Y02P90/30
Inventor 陶建峰覃程锦刘成良石岗余宏淦孙浩李彬雷军波
Owner SHANGHAI JIAO TONG UNIV
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