Systematic fatigue spectrum editing by fast wavelet transform and genetic algorithm

Abstract

AbstractFatigue testing is critical in order to establish the service life of load‐bearing components and structures. The extensive time associated with full fatigue spectrum testing can lead to prohibitive costs. A significant need exists for a fatigue load spectrum editing methodology, based on the mechanics of fatigue, that produces load spectra that can replicate service damage in laboratory testing and can lead to compressed testing times and reduced costs. In this work, a wavelet genetic (WAVEGEN) algorithm is developed to edit fatigue loading spectra using wavelet analysis to greatly reduce the length of a spectrum while retaining the same damage accumulation characteristics. In addition, an optimization protocol using a genetic algorithm is included within this process to automatically select the best wavelet editing parameters. The algorithm is designed to identify the most suitable wavelet type, filter, and level to optimally edit a given fatigue spectrum and ensure equivalence between edited and unedited spectra from a damage perspective. The algorithm was applied to two well‐known aircraft fatigue spectra: Fighter Aircraft Loading Standard for Fatigue evaluation (FALSTAFF) and Transport Wing Standard (TWIST). The proposed approach has demonstrated that both spectra can be compressed significantly even while ensuring equivalence from a damage perspective.