TY - GEN
T1 - Online monitoring of corrosion damage using ultrasonic guided wave tomography
AU - Rao, Jing
AU - Ratassepp, Madis
AU - Fan, Zheng
PY - 2017
Y1 - 2017
N2 - Corrosion of pressure vessels, storage tanks and pipelines is a significant problem in petrochemical and nuclear industries. Detecting and quantifying the wall thickness loss due to the corrosion damage is of growing interest. Conventional ultrasonic thicknessgauging methods are tedious and expensive, especially for inaccessible areas. Guided wave tomography offers good potential to monitor the remnant thicknesses of corrosion patches without accessing all points on the surface. It uses the dispersion characteristics of guided waves, and reconstructs the thickness map by the inversion of ultrasonic signals captured by a transducer array around the inspection area. This work applies a novel guided wave tomography method based on full waveform inversion (FWI) for the corrosion mapping. It uses a numerical forward model to predict the scattering of guided wave through corrosion defects, and an iterative inverse model to reconstruct the corrosion profile. At each iteration, numerical modeling is performed with the aim of least-squared minimization of the misfit between the modeled and the observed data. The FWI algorithm allows higher order diffraction and scattering to be considered in its numerical solver, thus can provide accurate inversion results. Numerical simulation and experiment have been carried out to validate the algorithm, showing excellent agreements. A guided wave tomography system is then designed to monitor different stages of forced electrochemical corrosion. The reconstructed thickness map match perfectly with the prediction using the Faradays Law, as well as the measurements from a laser profilemeter.
AB - Corrosion of pressure vessels, storage tanks and pipelines is a significant problem in petrochemical and nuclear industries. Detecting and quantifying the wall thickness loss due to the corrosion damage is of growing interest. Conventional ultrasonic thicknessgauging methods are tedious and expensive, especially for inaccessible areas. Guided wave tomography offers good potential to monitor the remnant thicknesses of corrosion patches without accessing all points on the surface. It uses the dispersion characteristics of guided waves, and reconstructs the thickness map by the inversion of ultrasonic signals captured by a transducer array around the inspection area. This work applies a novel guided wave tomography method based on full waveform inversion (FWI) for the corrosion mapping. It uses a numerical forward model to predict the scattering of guided wave through corrosion defects, and an iterative inverse model to reconstruct the corrosion profile. At each iteration, numerical modeling is performed with the aim of least-squared minimization of the misfit between the modeled and the observed data. The FWI algorithm allows higher order diffraction and scattering to be considered in its numerical solver, thus can provide accurate inversion results. Numerical simulation and experiment have been carried out to validate the algorithm, showing excellent agreements. A guided wave tomography system is then designed to monitor different stages of forced electrochemical corrosion. The reconstructed thickness map match perfectly with the prediction using the Faradays Law, as well as the measurements from a laser profilemeter.
UR - https://www.scopus.com/pages/publications/85032444303
U2 - 10.12783/shm2017/14055
DO - 10.12783/shm2017/14055
M3 - 会议稿件
AN - SCOPUS:85032444303
T3 - Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017
SP - 1747
EP - 1753
BT - Structural Health Monitoring 2017
A2 - Chang, Fu-Kuo
A2 - Kopsaftopoulos, Fotis
PB - DEStech Publications
T2 - 11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017
Y2 - 12 September 2017 through 14 September 2017
ER -