An integrated wireless system for dynamic strain monitoring of Einel-rad-Einelfahrwerk bogies for high-speed rail transport

As critical load-bearing components of high-speed trains, bogies endure multidirectional alternating loads during long-term operation, making their dynamic strain characteristics and structural integrity pivotal to operational safety. Therefore, real-time dynamic strain monitoring of bogies is essential for predicting and preventing high-speed train accidents. Compared to traditional Non-Destructive Testing and Evaluation (NDT&E) methods, Radio Frequency Identification (RFID) sensors are more suitable for Structural Health Monitoring (SHM) due to their wireless capabilities and cost-effectiveness. Here we propose a wireless real-time monitoring system that utilizes integrated RFID tags with microcontrollers and sensor modules for real-time dynamic strain monitoring of Einel-rad-Einelfahrwerk (EEF) bogies. The microcontroller converts analog signals into digital, enhancing environmental interference resistance. The RFID tags wirelessly transmit digital signals to readers and host computers. To verify accuracy and real-time dynamic strain detection capabilities of designed system, experiments were conducted in laboratory settings and on-site with high-speed train bogies. Experimental results validate the system's 200 Hz dynamic strain detection capability with a 70 cm operational range. Laboratory calibrations within 600-1400 με exhibited absolute errors <19.39 με (2.07%). On-site tests on bogies revealed absolute errors of 5.08 με (7.50%) for axle strain and 7.59 με (7.66%) for wheel hub lateral strain under operational conditions.

Authors

Institutions

Publication Details

Journal
Communications Engineering
Published
2025-05-14
DOI
https://doi.org/10.1038/s44172-025-00429-y
Citations
2
Primary Topic
Railway Engineering and Dynamics
Type
article
Field-Weighted Citation Impact
1.43

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An integrated wireless system for dynamic strain monitoring of Einel-rad-Einelfahrwerk bogies for high-speed rail transport

Zhiwen Luo, FengLong Wang, Gui Yun Tian, Lai Wei et al.
2 citations
Communications Engineering
Railway Engineering and Dynamics
1.43
article

An integrated wireless system for dynamic strain monitoring of Einel-rad-Einelfahrwerk bogies for high-speed rail transport

Zhiwen Luo, FengLong Wang, Gui Yun Tian, Lai Wei, Yating Yu, Jing Zeng
article en
2 citations

Abstract

As critical load-bearing components of high-speed trains, bogies endure multidirectional alternating loads during long-term operation, making their dynamic strain characteristics and structural integrity pivotal to operational safety. Therefore, real-time dynamic strain monitoring of bogies is essential for predicting and preventing high-speed train accidents. Compared to traditional Non-Destructive Testing and Evaluation (NDT&E) methods, Radio Frequency Identification (RFID) sensors are more suitable for Structural Health Monitoring (SHM) due to their wireless capabilities and cost-effectiveness. Here we propose a wireless real-time monitoring system that utilizes integrated RFID tags with microcontrollers and sensor modules for real-time dynamic strain monitoring of Einel-rad-Einelfahrwerk (EEF) bogies. The microcontroller converts analog signals into digital, enhancing environmental interference resistance. The RFID tags wirelessly transmit digital signals to readers and host computers. To verify accuracy and real-time dynamic strain detection capabilities of designed system, experiments were conducted in laboratory settings and on-site with high-speed train bogies. Experimental results validate the system's 200 Hz dynamic strain detection capability with a 70 cm operational range. Laboratory calibrations within 600-1400 με exhibited absolute errors <19.39 με (2.07%). On-site tests on bogies revealed absolute errors of 5.08 με (7.50%) for axle strain and 7.59 με (7.66%) for wheel hub lateral strain under operational conditions.

Communications EngineeringVol. 4(1)
University of Electronic Science and Technology of China (CN), Southwest Jiaotong University (CN), Newcastle University (GB)
National Natural Science Foundation of China, Natural Science Foundation of Sichuan Province
Affordable and clean energy
Openalex Percentile: Top 20%
Railway Engineering and Dynamics
1.43
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.