Fault diagnosis based on support vector machines with parameter optimization by an ant colony algorithm

Since support vector machines (SVM) exhibit a good generalization performance in the small sample cases, these have a wide application in machinery fault diagnosis. However, a problem arises from setting optimal parameters for SVM so as to obtain optimal diagnosis result. This article presents a fault diagnosis method based on SVM with parameter optimization by ant colony algorithm to attain a desirable fault diagnosis result, which is performed on the locomotive roller bearings to validate its feasibility and efficiency. The experiment finds that the proposed algorithm of ant colony optimization with SVM (ACO—SVM) can help one to obtain a good fault diagnosis result, which confirms the advantage of the proposed ACO—SVM approach.