关键词: Renyi熵, 故障诊断, 滚动轴承, 经验模式分解, 支持矢量机

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.

Key words: Empirical mode decomposition, Fault diagnosis, Least square support vector machine Renyi-entropy, Rolling-bearings