ABSTRACT
Active Lamb-wave-based structural health monitoring techniques have been widely studied to inspect large structures using permanently installed arrays of sensors and actuators. Most of these methods depend on comparing baseline signals recorded from the structure before going into service and test signals acquired during inspection. Temperature changes affect the propagation of the wave in a nonlinear and mode-dependent manner. As a result, baseline comparison methods fail when the test and baseline signals are acquired at vastly different temperatures. Approximate methods that compensate for the effects of temperature on the waves using signal stretch models have been introduced in the literature. These methods are effective when the temperature changes are small and the propagation distances are short. However, they perform poorly when these conditions are not satisfied. Consequently, there is a need for better temperature compensation algorithms than presently available. This article presents a data-driven approach that separately compensates for the effects of temperature on different mode components of the sensor signals. The performance of the temperature compensation algorithm of this article is compared with that of a commonly used baseline signal stretch (BSS) algorithm using experimental signals measured from an aluminum panel and a unidirectional composite panel. Analysis results indicate that the method of this article outperforms the BSS algorithm for large temperature differences. The usefulness of the temperature compensation algorithm is further validated by demonstrating the ability of compensated signals to accurately reconstruct anomaly maps associated with damaged composite structures.
