Current and future trends in naval craft design are leaning toward the development of high-speed and high-performance vessels. Lack of information on wave-induced loads for these vessels presents a challenge in ensuring their safety that is best tackled with monitoring operational loads and detecting damage via structural health monitoring (SHM) systems. These monitoring systems, however, require efficient statistical and probabilistic procedures that are able to effectively treat the uncertainties inherent in the massive volumes of collected data and provide interpretable information regarding the reliability and condition of the craft structure. In this article, an approach for using SHM data in the reliability analysis and damage detection in high-speed naval craft (HSNC) under uncertainty is presented. This statistical damage detection technique makes use of vector autoregressive modeling for detection and localization of damage in the ship structure. The methodology is illustrated on an HSNC, HSV-2. Data obtained from seakeeping trials of HSV-2 were treated as the SHM data mentioned above.
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