Fault Diagnosis Strategy for Magnetostrictive Sensor
Test Platform Based on Steady State Recognition

ZHANG Hongda; MENG Xianjun; HAN Weifeng; ZHANG Chanjuan; JIANG Yongcheng; ZOU Qiang

doi:10.3973/j.issn.2096-4498.2025.04.007

Tunnel Construction >

2025 , Vol. 45 >Issue 4: 730 - 739

DOI: https://doi.org/10.3973/j.issn.2096-4498.2025.04.007

Fault Diagnosis Strategy for Magnetostrictive Sensor Test Platform Based on Steady State Recognition

ZHANG Hongda ,
MENG Xianjun ,
HAN Weifeng ,
ZHANG Chanjuan ,
JIANG Yongcheng ,
ZOU Qiang

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(1. Equipment Branch of China Railway Tunnel Group Co., Ltd., Luoyang 471000, Henan, China; 2. State Key Laboratory of Shield Machine and Tunneling Technology, Zhengzhou 450001, Henan, China; 3. School of Engineering Technology, Tianjin Agricultural University, Tianjin 300392, China)

Online published: 2025-04-20

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Abstract

Incorporating system state data from the dynamic response phase of a magnetostrictive sensor testing platform into training datasets for fault diagnosis strategies is challenging, often resulting in low reliability of data-driven diagnostic results. To address this issue, a data-driven fault diagnostic strategy based on steady-state identification is proposed. First, a steady-state identification strategy is designed to obtain system-state data generated during the steady-state operation of the test platform. Support vector data description (SVDD), which can handle single-class classification problems, is used for steady-state identification of the test platform. During the training process, particle swarm optimization (PSO) is utilized to optimize the hyperparameters of the SVDD. Second, a support vector machine (SVM) optimized by PSO is adopted to perform fault diagnosis based on the state data collected during the steady-state operation of the test platform to enhance fault diagnosis accuracy and the reliability of the diagnostic results. Finally, a simulation model of the test platform is established using the Simscape module in Simulink to validate the proposed steady-state identification-based fault diagnostic strategy. The simulation results demonstrate that compared with the SVM-based data-driven fault diagnosis strategy without steady-state identification, the proposed strategy achieves an average accuracy improvement of 5% across different modes, confirming the effectiveness of the proposed method for fault diagnosis in the test platform.

Key words： shield; magnetostrictive sensor test platform; steady-state identification; fault diagnosis

Cite this article

ZHANG Hongda , MENG Xianjun , HAN Weifeng , ZHANG Chanjuan , JIANG Yongcheng , ZOU Qiang .

Fault Diagnosis Strategy for Magnetostrictive Sensor Test Platform Based on Steady State Recognition

[J]. Tunnel Construction, 2025 , 45(4) : 730 -739 . DOI: 10.3973/j.issn.2096-4498.2025.04.007

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