时变滤波经验模态分解与对称差分解析能量算子在轴承故障诊断中的应用

Application of Time-varying Filtering Empirical Mode Decomposition and Symmetrical Difference Analytic Energy Operator in Fault Diagnosis of Bearings

经验模态分解算法作为一种经典的模态分解技术,在许多科研领域得到了广泛应用。然而分离问题和间歇性问题仍未解决,这可能导致模态混叠现象。因此,采用一种基于时变滤波的改进的经验模态分解方法。该种改进的模态分解算法可以确保了一个故障信号被精确地分解为多个含有独特故障特征的分信号。然后,采用对称差分解析能量算子对含有故障特征的分信号进行故障特征提取。实验结果表明,该方法是一种有效的轴承故障诊断工具。

Empirical mode decomposition(EMD),as a classical mode decomposition technique,has been widely used in quite a few scientific fields.However,the separation problem and the intermittent problem,which can lead to mode mixing,still remain unsolved.Therefore,a fault diagnosis method of rolling bearings based on time-varying filtering for empirical mode decomposition(TVF-EMD)is introduced in this paper.The TVF-EMD is in fact an improved modal decomposition algorithm,which can improve the mode mixing in comparison with EMD and ensure that the fault signal can be decomposed into several sub-signals precisely and each one of them includes a unique fault characteristic.Then,the fault feature extraction is done for these sub-signals using the symmetrical difference analytic energy operator(SD-AEO).The experimental results show that the proposed method is a powerful and efficient tool for bearing fault diagnosis.