A time-frequency analysis-based signal processing study for detecting active corrosion in aluminum plate-like structure utilizing the broadband piezoelectric wafer active sensors is presented in this article. Tests were conducted on an aluminum plate with a network of sensors installed on one side of the plate for Lamb wave generation and reception. The corrosion was emulated as material loss of an area of 50 × 38 mm2 on the opposite side of the plate. The corroded area resulted in a thickness loss on the plate and a change in wave propagation as well. The experimental data were first evaluated by a statistical damage index (DI) based on root mean square values and then the Cohen's class motivated cross-time-frequency analysis. The cross-time-frequency analysis was found more reliable and precise for detecting the corrosion progression when compared to the DI method. Not only can the proposed metric correctly evaluate the phase difference of specific frequency and time, it also carries useful information of phase difference, which is strongly correlated to the physics of corrosion detection using Lamb waves. Novel aspects of this study include a sensing approach that can sense corrosion damage on both external and internal surfaces of a given structure, the employment of effective tuning in corrosion detection, and using cross-time-frequency analysis to quantitatively evaluate thickness loss. Though the corrosion studied herein is an idealized and simplified situation, the subject work on phase difference and cross-time-frequency analysis is useful first-step effort and opens a new way to perform Lamb wave-based corrosion detection. The results presented in this article combine easy-to-examine corrosion assumptions together with low-frequency antisymmetric Lamb wave analysis to provide a stepping stone for more complicated analysis needed for further real life corrosion assessment.
Bibliographical noteFunding Information:
This material is based upon study supported by the National Science Foundation under Grant #CMS-0408578 and Grant #CMS-0528873 with Dr Shih-Chi Liu as the program director; and Grant #ECCS-0747681 with Dr George Maracas as the program director. The authors appreciate proof reading of the manuscript by Mr David Coats and Mr Patrick Pollock.
All Science Journal Classification (ASJC) codes
- Mechanical Engineering