基于软筛分停止准则的改进经验模态分解及其在旋转机械故障诊断中的应用

Improved EMD with a Soft Sifting Stopping Criterion and Its Application to Fault Diagnosis of Rotating Machinery

筛分停止准则是影响经验模态分解在高速列车旋转机械故障诊断准确率的关键因素之一。目前普遍采用预先设定阈值的方法,不具有自适应性,导致经验模态分解易出现模态混叠的问题,进而影响故障诊断结果。鉴于充分论证筛分停止准则对经验模态分解结果的影响,提出一种能够自适应控制筛分过程的软筛分停止准则,用于抑制模态混叠问题,提高经验模态分解精度和效率。针对目标信号,该准则通过定义一个刻画全局能量和局部冲击的目标函数,结合启发式搜索机制,实现每次筛分过程中筛分迭代次数的优化,进而保障经验模态分解获取最优分解结果。利用仿真数据和凯斯西储大学轴承基准数据集,对改进的经验模态分解与两种传统实现方法在不同的分解和诊断性能维度上进行对比讨论。最后,将提出的改进经验模态分解方法成功应用于高速列车旋转机械模拟试验台的故障诊断案例中。

The sifting stopping criterion of empirical mode decomposition is one of the key factors affecting the accuracy of fault diagnosis of rotating machinery in high-speed trains.The current method of determining thresholds in advance is generally adopted,which is not adaptive.Such methods lead to the problem of mode mixing in empirical mode decomposition,which affects the fault diagnosis results.The influence of sifting stopping criterion on the empirical mode decomposition results are fully demonstrated,and then a soft sifting stopping criterion that can adaptively monitor the sifting process is proposed.This criterion is used to suppress the mode mixing problem and improve the accuracy and efficiency of empirical mode decomposition.Aiming at the target signal,the criterion defines an objective function that describes the global energy and local impact characteristics,and combines a heuristic mechanism to realize the optimization of the sifting iterations number in each sifting process,so as to guarantee the empirical mode decomposition to obtain the optimal decomposition results.Based on the simulation data and the Case Western Reserve University Bearing Data,the improved empirical mode decomposition is compared with the two traditional methods in different decomposition and diagnostic performance dimensions.Finally,the proposed improved EMD is successfully applied to the fault diagnosis case of rotating machine simulation test rig of high-speed trains.