Adaptive-Neuro-Fuzzy-Based Information Fusion for the Attitude Prediction of TBMs

He, Boning; Zhu, Guoli; Han, Lei; Zhang, Dailin

doi:10.3390/s21010061

Cited by 9 publications

(2 citation statements)

References 49 publications

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“…In recent years, with the rapid development of information technology and the popularization and application of big data, machine learning, as a powerful tool for data analysis and recognition, has achieved remarkable results in various fields [ 11 , 12 , 13 , 14 , 15 ]. In the field of TBMs, machine learning has revolutionized traditional engineering and construction methods [ 16 , 17 , 18 , 19 ]. Machine learning algorithms can analyze large amounts of data intelligently, providing new insights for the operation and maintenance of TBMs.…”

Section: Introductionmentioning

confidence: 99%

SSFLNet: A Novel Fault Diagnosis Method for Double Shield TBM Tool System

Zhou,

Liu,

et al. 2024

Sensors

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In tunnel boring projects, wear and tear in the tooling system can have significant consequences, such as decreased boring efficiency, heightened maintenance costs, and potential safety hazards. In this paper, a fault diagnosis method for TBM tooling systems based on SAV−SVDD failure location (SSFL) is proposed. The aim of this method is to detect faults caused by disk cutter wear during the boring process, which diminishes the boring efficiency and is challenging to detect during construction. This paper uses SolidWorks to create a complete three−dimensional model of the TBM hydraulic thrust system and tool system. Then, dynamic simulations are performed with Adams. This helps us understand how the load on the propulsion hydraulic cylinder changes as the TBM tunneling tool wears to different degrees during construction. The hydraulic propulsion system was modeled and simulated using AMESIM software. Utilizing the load on the hydraulic propulsion cylinder as an input signal, pressure signals from the two chambers of the hydraulic cylinder and the system’s flow signal were acquired. This enabled an in−depth exploration of the correlation between these acquired signals and the extent of the tooling system failure. Following this analysis, a collection of normal sample data and sample data representing different degrees of disk cutter abrasions was amassed for further study. Next, an SSFL network model for locating the failure area of the cutter was established. Fault sample data were used as the input, and the accuracy of the fault diagnosis model was tested. The test results show that the performance of the SSFL network model is better than that of the SAE−SVM and SVDD network models. The SSFL model achieves 90% accuracy in determining the failure area of the cutter head. The model effectively identifies the failure regions, enabling timely tool replacement to avoid decreased boring efficiency under wear conditions. The experimental findings validate the feasibility of this approach.

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Section: Introductionmentioning

confidence: 99%

SSFLNet: A Novel Fault Diagnosis Method for Double Shield TBM Tool System

Zhou,

Liu,

et al. 2024

Sensors

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“…Wang et al conducted kinematic analysis of the thrust system of the TBM and developed a control system for the multiple thrust cylinder system of the TBM, and this is useful for controlling the TBM attitude [16]. In addition, other studies conducted by researchers are helpful with this problem [7,[17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A Theoretical Method and Model for TBM Tunnelling Trajectory Adjustment

Wang,

Zhang

2023

Applied Sciences

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TBM tunnelling trajectory adjustment is a significant issue in guaranteeing a tunnel project. This paper proposes a theoretical method of trajectory adjustment aimed at synchronously reducing bias angle and distance. The proposed method can be divided into three steps. First, the need for manual intervention in the tunnelling trajectory is determined based on the current bias angle and distance. Next, if manual intervention is required, a theoretical relationship is established using the current bias angle and distance as an input and the rotated angle of the TBM main beam as an output. Finally, a series of constraints according to the normal working conditions of the TBM are incorporated into the method. These constraints ensure that the method remains within the acceptable range of TBM operation. Using mechanical parameters such as the cutterhead diameter and cutter spacing of the TBM used in the sixth section of the Qingdao Metro Project, a model suitable for the project is established, and the model is modified by the tunnelling trajectory data collected in the field. This method provides a reference for TBM drivers to use to correct the tunnelling trajectory while considering the specific conditions of the project.

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Data-driven multi-step robust prediction of TBM attitude using a hybrid deep learning approach

Wang

Zhang

et al. 2023

Advanced Engineering Informatics

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Adaptive-Neuro-Fuzzy-Based Information Fusion for the Attitude Prediction of TBMs

Cited by 9 publications

References 49 publications

SSFLNet: A Novel Fault Diagnosis Method for Double Shield TBM Tool System

SSFLNet: A Novel Fault Diagnosis Method for Double Shield TBM Tool System

A Theoretical Method and Model for TBM Tunnelling Trajectory Adjustment

Data-driven multi-step robust prediction of TBM attitude using a hybrid deep learning approach

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