Using ensemble techniques to advance adaptive one‐factor‐at‐a‐time experimentation

Abstract

AbstractEnsemble Methods are proposed as a means to extendbiAdaptive One‐Factor‐at‐a‐Time (aOFAT) experimentation. The proposed method executes multiple aOFAT experiments on the same system with minor differences in experimental setup, such as ‘starting points’. Experimental conclusions are arrived at by aggregating the multiple, individual aOFATs. A comparison is made to test the performance of the new method with that of a traditional form of experimentation, namely a single fractional factorial design which is equally resource intensive. The comparisons between the two experimental algorithms are conducted using a hierarchical probability meta‐model and an illustrative case study. The case is a wet clutch system with the goal of minimizing drag torque. In this study, the proposed procedure was superior in performance to using fractional factorial arrays consistently across various experimental settings. At the best, the proposed algorithm provides an expected value of improvement that is 15% higher than the traditional approach; at the worst, the two methods are equally effective, and on average the improvement is about 10% higher with the new method. These findings suggest that running multiple adaptive experiments in parallel can be an effective way to make improvements in quality and performance of engineering systems and also provides a reasonable aggregation procedure by which to bring together the results of the many separate experiments. Copyright © 2011 John Wiley & Sons, Ltd.