Study on a hybrid SVM model for chiller FDD applications

This study investigates a hybrid model that combines support vector machine (SVM) with genetic algorithm (GA) and parameter tuning technique for chiller FDD applications, where GA is responsible for searching potential feature subsets and SVM behaves both as an FDD tool and an evaluation method for feature selection.