The propagation characteristics of ultrasonic guided waves can be used to achieve the measurement of physical parameters such as position, stress, fluid viscosity, as well as the non-destructive testing and structural health monitoring of important structural safety. It has great potential for application in the fields of industry, transportation and biomedical engineering. Due to the fact that guided waves can propagate over relatively long distances and are sensitive to defects, guided waves can long-range evaluate the integrity of pipelines, bars, high-speed railway switch rails, etc. from one single test point, and also shown great potential in measuring blood viscosity.
In-service high-speed railway switch rail undertakes more fatigue loads and are more prone to cause catastrophic accidents than the stock rail, thus endangering the safety of people's lives and property. In the condition-based maintenance of high-speed railway, ultrasonic guided wave on-line monitoring technology is widely used in judging real-time operating conditions; however, it always generates large amounts of data. Too much data brings significant challenges, such as too many unnecessary costs of energy, storage, and network bandwidth in the structural health monitoring of switch rails, making it challenging to realize embedded sensor networks with high durability and low power consumption.
Professor Zhifeng Tang’s group proposed a novel data compression and reconstruction method to meet the challenges and reduce the amount of data transmitted by sensor networks and maintain their accuracies simultaneously，as well as multi-feature integration and automatic classification algorithm for switch rail damage using guided wave monitoring signals. A large number of experiments on the switch rails were carried out to evaluate the proposed method. The results indicate that the proposed method is capable of identifying damage in challenging cases and is superior to conventional methods. Our guided wave monitoring system has been tested on the railway switch rail for a long time. This research was funded by the National Natural Science Foundation of China (Grant Nos. 51875511, U1709216) and the Technique Plans of Zhejiang Province (Grant No. 2019C03112).
Figure 1 Guided wave propagates in (a) a switch rail (left) and (b) a stock rail (right).
Figure 2 Guided wave monitoring system installs on switch rails and stock rails in Hangzhou Railway Station (Joint developed with Zheda Jingyi Corp and Railway Engineering Research Institute in CARS)
1. Liu W , Tang Z , Lv F , et al. Multi-feature integration and machine learning for guided wave structural health monitoring: Application to switch rail foot[J]. Structural Health Monitoring, Vol 20, Issue 4, 2021.( https://doi.org/10.1177/1475921721989577)
2. Tang Z , Liu W , Yan R , et al. Application of compressed sensing in the guided wave structural health monitoring of switch rails[J]. Measurement Science and Technology, 2021, 32(12).( https://iopscience.iop.org/article/10.1088/1361-6501/ac2316/meta)