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Smart Structures and Systems Volume 34, Number 5, November 2024 , pages 311-322 DOI: https://doi.org/10.12989/sss.2024.34.5.311 |
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Bayesian compressed sensing-based tomographic method for corrosion monitoring |
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Zengnian Xin and Ming Chang
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| Abstract | ||
| For airplanes, especially aging airplanes, corrosion is widespread. As the corrosion reaches a certain level, it may cause flight accidents. Lamb wave tomography (LWT), as one of the Structural Health Monitoring technologies, can be used to monitor the corrosion of aircraft structures. However, the LWT requires densely arranged sensors on both sides of the monitoring area and has the poor imaging quality. These disadvantages limit its application in aircraft corrosion monitoring. In view of this situation, this paper proposes Bayesian Compressed Sensing (BCS)-based tomographic method to monitor corrosion of aircraft structure. BCS-based tomographic method reduces the number of sensors by under-sampling the received lamb wave signal and utilizes a Bayesian formulation to perform the original signal reconstruction. Compared to conventional LWT, the new method has better imaging quality with fewer sensors. Compared to the improved Compressed Sensing (CS)-based tomographic, BCS-based tomographic has fewer imaging artifacts, and shorter imaging time. Simulation and experiment are carried out on aviation aluminum plate with corrosion to verify the new proposed method. The results show the advantages of the proposed BCS-based tomographic method. | ||
| Key Words | ||
| aircraft structure; Bayesian Compressed Sensing; corrosion monitoring; lamb wave tomography | ||
| Address | ||
| (1) Zengnian Xin: Research Center of Structural Health Monitoring and Prognosis, State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing, Republic of China; (2) Ming Chang: School of Electrical, Energy and Power Engineering, Yangzhou University, 88 South University Road, Yangzhou, Republic of China. | ||