PhD students

Yu Fung Lee (Joseph)

Monash University (Supervisor: Ye Lu)

PhD topic – Nonlinear vibro‐acousto‐ultrasonic waves for fatigue cracking detection in key rail components

Research summary

This project is looking to provide the advanced performance surveillance for in-service trains, in particular, damage detection for key components of train structures (such as the bogie frame). A diagnostic sensor network will be designed and integrated to activate and capture ultrasonic wave signals together with the vibration signals from the train in service. Outcomes of the project will lead to a practical solution to managing the safety and integrity of ageing trains.

Expected completion date

August 2020

Key achievements

• Quantitative evaluation of fatigue crack initiation and growth on joint-like steel structures based on nonlinear ultrasonic waves.
• High precision numerical simulation of fatigue crack growth for the joint of bogie frame.
• Identification of fatigue crack by nonlinear ultrasonic waves in the consideration of vibration caused by train.

Publications

Lee, Y. F., Lu, Y., and Guan, R. (2020). Nonlinear guided waves for fatigue crack evaluation in steel joints with digital image correlation validation. Smart Materials and Structures, 29(3), 035031.

Lee, Y. F., Lu, Y., Chiu, W.K. and Iman, S. 2019. Nonlinear guided waves detection for fatigue crack detection in a steel joint, in the Proceedings of the 12th International Workshop on Structural Health Monitoring, 10-12, September 2019, Stanford University, Stanford, CA, USA

Lee, Y., Lu, Y., Chiu, W. K., and Salehi, I. (2018). Numerical simulation of crack detection on key railway structures using guided waves. The 7th World Conference on Structural Control and Monitoring, &WCSCM, 22-25 July 2018, Qingdao, China.