Rolling-bearings fault diagnosis based-on empirical mode decomposition and least square support vector machine

Abstract

In order to extract equipment condition information effectively, a new fault diagnosis method is proposed based-on EMD(empirical mode decomposition) and LS-SVM(least square support vector machine), which takes vibration signals’ Renyi entropy, a complexity measure, as measure criterion. Firstly, vibration signals are decomposed into several IMFs (intrinsic mode functions), then the Renyi entropy of each IMF is computed and regarded as the input characteristic vectors of LS-SVM for fault classification. The rolling-bearings fault diagnosis examples prove the practicability of the method.