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. 2022 Oct 14;22(20):7795.
doi: 10.3390/s22207795.

Collaborative Damage Detection Framework for Rail Structures Based on a Multi-Agent System Embedded with Soft Multi-Functional Sensors

Xiao Cheng  1 , Daojin Yao  2 , Lin Yang  3 , Wentao Dong  2
Affiliations

Collaborative Damage Detection Framework for Rail Structures Based on a Multi-Agent System Embedded with Soft Multi-Functional Sensors

Xiao Cheng et al. Sensors (Basel). .

Abstract

With the rapid growth of railways in China, the focus has changed to the maintenance of large-scale rail structures. Multi-agent systems (MASs) based on wireless sensor network (WSNs) with soft multi-functional sensors (SMFS) are adopted cooperatively for the structural health monitoring of large-scale rail structures. An MAS framework with three layers, namely the sensing data acquisition layer, sensor data processing layer, and application layer, is built here for collaborative data collection and processing for a rail structure. WSN nodes with strain, temperature, and piezoelectric sensor units are developed for the continuous structural health monitoring of the rail structure. The feature data at different levels are extracted for the online monitoring of the rail structure. Experiments carried out at the Rail Transmit Base at East China Jiaotong University verify that the WSN nodes with SMFS are successfully assembled onto a 100-m-long track for damage detection. Based on the sensing data and feature data, a neural network data fusion agent (DFA) is applied to calculate the damage index value of the track for comprehensive decisions regarding rail damage. The use of WSNs with multi-functional sensors and intelligent algorithms is recommended for cooperative structural health monitoring in railways.

Keywords: damage detection; multi-agent system; soft multi-functional sensors; structural health monitoring; wireless sensor network.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Long-distance rail structural health monitoring based on a WSN integrated with SMFS. (a) WSN nodes are laid out on the railway; (b) Monitoring terminal for long-distance rail.
Figure 2
Figure 2
The monitoring architecture of large-scale rail structure based on MAS.
Figure 3
Figure 3
A structural diagram of an SA.
Figure 4
Figure 4
The internal structure and principle of the SPA.
Figure 5
Figure 5
The internal structure and principle of the DFA.
Figure 6
Figure 6
Online monitoring and damage identification of rail defect data by the MAS.
Figure 7
Figure 7
(a) Lamb guided wave and (b) echo signal for active damage detection of the rail.
Figure 8
Figure 8
Strain sensor data from the strain sensor agent: (a) variable resistance value with the external strain; (b) repeated load and unload process for the strain sensor.
Figure 9
Figure 9
Temperature sensor data from the temperature sensor agent.
Figure 10
Figure 10
Data fusion framework for track damage detection.
Figure 11
Figure 11
(a) Experimental setup for the online structural health monitoring of rail structures based on an MAS with SMFS; (b) optical image of WSN with SMFS; (c) optical image of SMFS.
Figure 12
Figure 12
The strain and temperature sensing data from the SMFS embedded into WSN nodes for cooperative data collection from the track.
Figure 13
Figure 13
RMS value for the strain and temperature data extracted by the SPA.
Figure 14
Figure 14
RMS and aptitude information from the echo signal processed by the SPA.
Figure 15
Figure 15
RSSI values from WSN communication with different payloads.
Figure 16
Figure 16
Damage index values of the rail structure based on WSNs embedded with SMFS.
See this image and copyright information in PMC

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