机械故障的级联共振和EMD联合参数辨识方法

Identification method of mechanical fault based on CBSR and EMD joint parameter

针对强噪声背景下机械故障信号难以检测,参数辨识难度高的问题,提出了基于级联随机共振和经验模态分解的联合参数辨识方法。该方法利用EMD分层分解的思想,可以结合标准平均方差(Normalized Mean Squared Error,NMSE)准则筛选出最优IMF分量,最终实现原信号频率特征参数的准确拟合。实验结果表明,文中算法可有效消除随机共振处理后信号的边缘脉冲,进而实现信号频率的准确检测。在信噪比低于-15 dB时,算法的检测性能提升了约一个数量级,在固定检测差错概率为10^(-3)时,算法的信噪比增益可达到8 dB。新算法对于机械故障信号中的频率参数辨识具有检测误差小、适应范围广泛的优势,在保证带来一定信噪比增益的同时,可实现工程器件状态的准确判断,对于提取机械系统的故障特征、识别故障类型以及进一步地排故检修具有重要意义。

The classical traditional detection methods for mechanical fault under strong noise background have lots of problems such as low accuracy,high difficulty. Aiming at these problems,an alternative reconstitution method based on the EMD theory and CBSR is proposed in the paper. This algorithm adopts the digestion in depth to obtain the peculiar IMF component,which is used to fit the cycle characteristics of initial signals. The simulation results indicate that the novel algorithm could effectively get rid of edge pulses,achieve the implementation of CBSR signals＇ frequency detection need. When the SNR is below- 15 d B,the detection performance could be improved by one order of magnitude,and when the detection error rate is fixed at 10^（-3）,the proposed method could get at least one SNR gains of 8d B. It could be guaranteed that the cascaded system brings a certain signal to noise ratio gain,at the same time,can detect characteristic parameters such as signal frequency. The application scope and engineering realization of expanding nonlinear detection theory are of great value.