基于IESOA-BP的滚动轴承故障诊断

Fault diagnosis of rolling bearing based on IESOA-BP

在机械设备故障诊断中,输入特征向量的差异会影响诊断精度,为了提高智能制造模式下滚动轴承故障诊断的准确性和可靠性,提出一种基于变分模态分解(VMD)和时频域熵的故障特征提取结合改进的白鹭群算法(IESOA)优化BP神经网络的故障诊断方法。首先,借助变分模态分解,成功解决模式混叠的问题;其次,提取各模态分量的时域香农熵和频域频谱熵构造故障特征向量,作为故障诊断模型的输入;再次,引入霍尔顿序列初始化白鹭种群,增强白鹭群优化算法的全局寻优能力,构建改进的白鹭群算法以优化BP神经网络(IESOA-BP),最后用美国凯斯西储大学轴承数据集进行仿真;研究结果表明,VMD加时频域熵表征故障特征信息更丰富;相较于传统BP、PSO-BP、SSA-BP、ESOA-BP、SCESOA-BP等方法,IESOA-BP方法在滚动轴承故障诊断方面表现出更高的分类准确率和更好的稳定性。

In order to improve the accuracy and reliability of rolling bearing fault diagnosis in intelligent manufacturing mode,a fault diagnosis method based on Variational Mode Decomposition(VMD)and time-frequency domain entropy combined with improved Egret Swarm Algorithm(IESOA)to optimize BP neural network was proposed.Firstly,with the help of variational mode decomposition,the problem of pattern aliasing was successfully solved.Secondly,the time-domain Shannon entropy and frequency-domain spectral entropy of each modal component were extracted to construct fault feature vectors as input to the fault diagnosis model.Thirdly,the Halton sequence was introduced to initialize the egret population,the global optimization ability of the egret population optimization algorithm was enhanced,and the improved egret population algorithm was constructed to optimize the BP neural network(IESOABP),and finally the bearing dataset of Case Western Reserve University in United States was used for simulation.The results show that the entropy in the frequency domain of VMD time-added is more abundant in the characterization of fault characteristics.Compared with the traditional methods such as BP,PSO-BP,SSA-BP,ESOA-BP and SCESOABP,the IESOA-BP method shows higher classification accuracy and better stability in the fault diagnosis of rolling bearings.