IAO优化SVM的电机滚动轴承故障诊断

对于当前存在电机滚动轴承多种类型故障分类准确率不高的现象，提出一种改进天鹰优化算法（IAO）优化支持向量机（SVM）的电机滚动轴承故障诊断方法。首先，介绍了基本天鹰优化算法，然后引入Tent混沌映射和自适应权重对其改进，提高收敛速度，防止陷入局部最优；其次，对10种状态下的滚动轴承故障时域信号样本进行VMD分解，得到不同状态的时频域特征组成特征样本集。最后，利用IAO算法对支持向量机的惩罚参数(c)和核参数(g)进行优化，从而构建IAO-SVM滚动轴承故障诊断模型。最终结果表明，IAO-SVM诊断模型对电机滚动轴承10种状态下的故障诊断准确率最高达100%。

Fault diagnosis of motor rolling bearing based on IAO optimized SVM

In order to solve the problem that the accuracy of multi-type fault classification of motor bearing is not high, a fault diagnosis method of motor bearing based on the improved Aquila optimization algorithm (IAO) is proposed, which is used to optimize the Support vector machine of motor bearing.Firstly, the basic Aquila optimization algorithm is introduced, and then Tent chaotic map and adaptive weight are introduced to improve the algorithm. Secondly,VMD is performed on the time domain signal samples of rolling bearing faults under 10 states, and the time and frequency domain features of different states are obtained. Finally, the penalty parameter (C) and kernel parameter (g) of support vector machine were optimized by the IAO algorithm, so as to construct the IAO-SVM rolling bearing fault diagnosis model. The final results show that the IAO-SVM model has a high accuracy of 100% in fault diagnosis under 10 states of motor rolling bearings.