Abstract Development of the fault detection and diagnosis (FDD) for chiller systems is very
important for improving the equipment reliability and saving energy consumption. The results …

The accuracy of a support vector machine (SVM) classifier is decided by the selection of
optimal parameters for SVM. An artificial chemical reaction optimization algorithm (ACROA) is a …

This study investigates a novel method for roller bearing fault diagnosis based on local
characteristic‐scale decomposition (LCD) energy entropy, together with a support vector …

It is clearly known that support vector machine (SVM) parameters have significant effects on
the accurate rate of classification result. Adjusting the SVM parameters improves its …

Support vector machine (SVM) parameter optimization has always been a demanding task
in machine learning. The chemical reaction optimization (CRO) method is an established …

Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller
bearing and scarcity of fault samples, a novel method is presented and applied to roller …

In this paper, a hybrid approach that combines a population‐based method, adaptive elitist
differential evolution (aeDE), with a powerful gradient‐based method, spherical quadratic …

In this paper, a sliding mode control (SMC) system based on combining chemical reaction
optimization (CRO) algorithm with radial basis functional link net (RBFLN) for an n-link robot …

SVM parameters have serious effects on the accuracy rate of classification result. Tuning
SVM parameters is always a challenge for scientists. In this paper, a SVM parameter …

This study proposes a new method to diagnose roller bearing failure, in which we use EEMD
(Ensemble Empirical Mode Decomposition) method combined with the singular value …