Guided waves have been studied for monitoring defects on switch-rails. However, few researchers study monitoring of spallings. Previous studies on switch-rail damage detection are not suitable for this kind of damage at the edge of the rail web. In addition, wired configurations have the challenges of power supply and more cost. This paper proposes a time-frequency analysis based algorithm that is used to locate spallings. The algorithm is verified through an improved wireless based structural health monitoring (SHM) platform. The excitation frequency of 108 kHz is chosen as a compromise between larger wave velocity differences and smaller detectable damage sizes. A total of 4 piezoelectric (PZT) devices make up one actuator-sensor array. 2 PZT devices are placed at 2 sides of the rail web and both of them have the same excitation direction. The other 2 devices are mounted at the top of the rail web to measure spalling induced waves according to the cloud charts. The arrival time, frequency, and wave velocity features of the main modes in transmission and reflection waves are extracted. Then these parameters are substituted in the proposed algorithm to predict the spalling location. Simulation and epoxy bonding based experimental results show that the proposed method can identify a 15 mm length spalling at different locations. Finally, attention is paid to the mode identification errors that influence spalling localization. The effects of curvature on guided waves in switch-rails are analyzed and considered to be the cause of the errors.
- Time-frequency analysis
- mode conversion
- spalling localization
ASJC Scopus subject areas
- Electrical and Electronic Engineering