MCKD与改进的LSSVM在滚动轴承故障诊断中的应用

Application of MCKD and improved LSSVM in fault diagnosis of rolling bearing

针对滚动轴承运行过程中故障难以识别的问题,提出一种最大相关峭度解卷积与改进的最小二乘支持向量机的故障诊断方法。该方法首先利用最大相关峭度解卷积提取不同运行状态下轴承特征信息,然后利用最小二乘支持向量机对提取的特征信息进行监督学习,同时利用改进的布谷鸟搜索算法优化最小二乘支持向量机的核参数和惩罚因子在寻优过程中陷入局部最优、收敛精度差的问题,提升故障诊断的识别率。实测不同运行状态的轴承数据验证方法的有效性,实验结果表明,改进后的算法可有效准确识别滚动轴承各类状态,是一种可靠的轴承故障诊断方法。

Aiming at the problem that the fault is hard to identify during the operation of the rolling bearing, a fault diagnosis method of maximum correlation kurtosis deconvolution and improved least squares support vector machine is proposed. First of all, the method uses the maximum correlation kurtosis deconvolution to extract bearing vibration signals under different operating condi- tions. Then the least square support vector machine is used to supervise the extracted vibration signals. At the same time, improved cuckoo search algorithm is used to solve the problem that the kernel parameters and penalty factors of LSSVM fall into the local optimum and the convergence accuracy is poor in the optimization process, and improve the recognition rate of fault diagnosis. Bearing data were measured in different running states to verify the effectiveness of the method. The experimental results show that improved algorithm can effectively identify all types of rolling bearing status with high accuracy. It was a reliable method of bearing fault diagnosis.