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中文摘要：
为了有效提取设备状态信息，提出一种Renyi熵复杂性测度下的经验模式分解（Empirical mode decomposition，EMD）和最小二乘支持矢量机（Least square support vector machine，LS—SVM）的故障诊断方法。该方法先对振动信号进行EMD分解，得到多个基本模式分量（Intrinsic mode function,IMF）后，求出表征故障信息的若干个IMF的Renyi熵，再将其作为特征矢量输入LS-SVM进行故障分类。一个滚动轴承故障诊断实例说明该种方法的有效性。
英文摘要：
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.
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Rolling-bearings fault diagnosis based-on empirical mode decomposition and least square support vect
Study on non-linear filter characteristic and engineering application of cascaded bistable stochasti
Fault identification and classification of rolling element bearing based on time-varying autoregress
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