Satisfying Statistical Constraints in Preparing Edited Variable Amplitude Loading History Using Genetic Algorithm

A major concern that surfaces when performing the segment-based fatigue data editing technique is to certify that the values of two global statistics (root mean square and kurtosis) of the edited load history are within an acceptance interval whilst maximizing the data reduction rate and minimizing the loss in damage. The root mean square (rms) quantifies an overall energy underlying the history whilst kurtosis is important to identify impulsive character. In this paper, the stochastic Genetic Algorithm (GA) is employed as a post processing tool that helps the edited history satisfy the statistical requirements with minimum cost i.e. small decrement in the initial reduction rate. Consider the initial version of edited history being composed of high fatigue damage segments resulted from the non-overlapping segmentation method. In a case that the history does not comply with the statistical requirements, then importing a subset of low segments into the present edited history might reverse the outcome. Thus, the GA aims to search for the smallest subset that turns the history into fulfilling the rms and kurtosis needs without affecting the reduction rate too much. Experimental results show the capability of the proposed method in making the edited history fit the statistical constraints without imposing harm on the overall fatigue damage value.