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Shield tunneling machine auxiliary cruise method based on LSTM neural network

A technology of neural network and neural network model is applied in the field of auxiliary cruise of shield machine to achieve the effect of reducing the need for experience

Inactive Publication Date: 2019-08-20
XI AN JIAOTONG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, due to the complex and changeable geological conditions, the shield machine has many control parameters (penetration, screw machine speed, cutterhead speed, propulsion speed, cutterhead power, soil pressure, cutterhead torque and total thrust, etc.), the traditional The time series prediction algorithm is difficult to cope with the coupling of geological parameters and shield machine operating parameters

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  • Shield tunneling machine auxiliary cruise method based on LSTM neural network
  • Shield tunneling machine auxiliary cruise method based on LSTM neural network
  • Shield tunneling machine auxiliary cruise method based on LSTM neural network

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Embodiment Construction

[0039] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0040] refer to figure 1 , an LSTM neural network-based shield machine assisted cruise method, comprising the following steps:

[0041] 1) Collect the cruising parameter data of the shield machine in the early construction process. The cruising parameter data include penetration D, cutterhead speed Vt, propulsion speed Va, cutterhead power P, cutterhead torque M, and total thrust F. The disc torque M and the total thrust F show a positive correlation, and the cruise parameters and geological conditions are coupled with each other; use the z-score method to preprocess the cruise parameter data to obtain the penetration D, cutterhead speed Vt, propulsion speed Va, Training time series D of cutterhead power P, cutterhead torque M and total thrust F = {D 1 ,D 2 ,D 3 ,...,D T}, Vt={Vt 1 , Vt 2 , Vt 3 ,...,Vt T}, Va={Va 1 , Va 2 , Va 3 ,...,Va ...

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Abstract

A shield tunneling machine auxiliary cruise method based on an LSTM neural network comprises the steps that cruise parameter data of a shield tunneling machine are collected online, a training time sequence is obtained through preprocessing, and a training data set of an LSTM neural network model is obtained through division; then an LSTM neural network model is trained, and parameters of the LSTMneural network model are updated; and finally a cruise parameter at the next moment is predicted by using the trained LSTM neural network model. According to the method, parameters of the neural network are continuously trained to fit the characteristics of the whole geology-shield tunneling machine; and main operation parameters of the shield tunneling machine are predicted, and the requirementfor experience of operators is lowered.

Description

technical field [0001] The invention belongs to the technical field of shield machine assisted cruising, and in particular relates to a shield machine assisted cruising method based on an LSTM (long short-term memory network) neural network. Background technique [0002] The shield machine is a special mechatronic equipment for excavating tunnels. It is a key equipment in the fields of rail transit, water conservancy projects, highways and railways, and underground comprehensive pipe corridors. It plays a vital role in the process of urban modernization. As a special mobile processing workshop, the shield machine has a large structure, is highly integrated, has the characteristics of harsh working environment, and is prone to failure. The failure of the shield machine has an important impact on the overall construction progress, and even leads to the invalidation of the designed line and the scrapping of the entire tunnel; in addition, the geological conditions of the shield...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06Q10/04
CPCG06Q10/04G06N3/049G06N3/084G06N3/08G06N3/048
Inventor 徐光华况佳臣张四聪王博陈龙庭
Owner XI AN JIAOTONG UNIV