基于多尺度排列熵和IWOA-SVM的滚动轴承故障诊断

Rolling bearing fault diagnosis based on multi-scale permutation entropy and IWOA-SVM

针对滚动轴承信号表现出的非线性和非平稳性特征问题,合理的特征选择可提高故障诊断率,提出基于多尺度排列熵(MPE)与改进鲸鱼算法(IWOA)优化支持向量机(SVM)的故障诊断模型。首先,通过变分模态分解(VMD)进行信号降噪预处理,计算多尺度排列熵进行信号特征重构;其次,引入惯性动态权重对鲸鱼算法进行改进,通过训练SVM参数,建立IWOA-SVM故障诊断模型;最后用美国凯斯西储大学轴承数据集进行仿真。结果表明,相较于多尺度熵,MPE可表征的故障特征信息更加丰富,故障识别率提高了2.1%;与同类优化算法相比,采用IWOA对SVM进行优化的故障诊断模型,收敛速度快、训练时间短、故障识别精度高,可对滚动轴承进行有效诊断。

For the nonlinear and non-stationary characteristics of rolling bearing signals,reasonable feature selection can improve the fault diagnosis rate.A fault diagnosis model based on multi-scale permutation entropy(MPE)and improved whale algorithm(IWOA)was proposed to optimize support vector machine(SVM).Firstly,signal denoising was preprocessed by VMD,and multi-scale permutation entropy was calculated to reconstruct signal features.Secondly,inertial dynamic weights were introduced to improve the whale algorithm,and SVM parameters were trained to establish the IWOA-SVM fault diagnosis model.Finally,the bearing data set of Case Western Reserve University was used for simulation experiments.The results show that,compared with multi-scale entropy,MPE can represent more abundant feature information,and the fault recognition rate is improved by 2.1%.Compared with other optimization algorithms,the fault diagnosis model optimized by IWOA based on SVM has fast convergence speed,short training time and high fault recognition accuracy,which can effectively diagnose rolling bearings.