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

• Published in: Sensors (Basel, Switzerland) Switzerland), 2024-04, Vol.24 (8), p.2631
• Main Authors: Zhou, Peng, Liu, Chang, Xu, Jiacan, Ma, Dazhong, Wang, Zinan, He, Enguang
• Format: Article
• Language: English
• Subjects: Algorithms; Construction costs; Data analysis; Drilling & boring machinery; Efficiency; Engineering; Failure; failure localization; Fault diagnosis; Fractals; hydraulic thrust system; Hydraulics; Inventors; Machine learning; Maintenance costs; Methods; Neural networks; Signal processing; Simulation; Simulation methods; Software; tooling system; tunnel boring machine (TBM)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24082631

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.