摘要
针对中介轴承受背景噪声干扰导致故障特征提取困难且诊断识别率低的问题,提出一种基于河马算法(Hippopotamus Optimization Algorithm,HO)改进变模态分解(Variational Mode Decomposition,VMD)、并结合第一层宽卷积核深度卷积神经网络(Deep Convolutional Neural Networks with Wide First-layer Kernel,WDCNN)的故障诊断方法。该方法首先综合包络谱能量、峭度和排列熵构建多维度联合指标作为适应度函数,通过河马算法寻优确定VMD关键变量参数,其次,将原始信号分解后,根据相关系数-样本熵准则筛选有效模态分量进行重构,最后,将重构信号作为样本输入WDCNN模型完成故障诊断。凯斯西储大学轴承数据集试验结果表明:重构信号多次诊断的准确率为98.30%,相较于支持向量机、反向传播神经网络与一维卷积神经网络诊断有更高的正确率;对于自建中介轴承故障模拟试验台数据的多次诊断的平均准确率为96.4%,多种分类方法准确率平均优化了18.9%,稳定性优化了52%。证明该提方法能够去除噪声等非线性干扰成分,有效提升故障诊断模型分类准确性。
Aiming at the issues of the intermediate shaft bearings,where background noise makes fault feature extraction difficult and diagnostic recognition rates low,a novel fault diagnosis approach is proposed.This method combines the Hippopotamus Optimization Algorithm(HO)improved Variational Mode Decomposition(VMD)with the Deep Convolutional Neural Networks with Wide First-layer Kernel(WDCNN).Initially,this method constructs a multi-dimensional joint index,formed by integrating envelope spectrum energy,kurtosis,and permutation entropy,serves as the fitness function.The HO algorithm is then applied to optimize and determine the key parameters of VMD.After decomposing the original signal,effective modal components are selected and reconstructed following the correlation coefficient-sample entropy criterion.Finally,the reconstructed signal is fed into the WDCNN model for fault diagnosis.Experimental results from the Case Western Reserve University bearing dataset show that the reconstructed signal achieves a diagnosis accuracy of 98.30%.It also outperforms support vector machines,backpropagation neural networks,and one-dimensional convolutional neural networks in terms of accuracy.For data from a self-built intermediate bearing fault simulation test bench,the average accuracy of multiple diagnoses is 96.4%,with the average accuracy of various classification methods optimized by 18.9%and stability by 52%.In conclusion,the proposed method can eliminate non-linear interference like noise and significantly enhance the classification accuracy of fault diagnosis models.
作者
田晶
谢肇阳
吕静宜
林政
TIAN Jing;XIE Zhaoyang;LYU Jingyi;LIN Zheng(School of Aero-Engine,Shenyang Aerospace University,Shenyang 110136,China)
出处
《推进技术》
北大核心
2026年第1期309-320,共12页
Journal of Propulsion Technology
基金
国家自然科学基金(12172231)
辽宁省兴辽英才计划(XLYC2203042)
辽宁省自然科学基金(2023-BS-146)。
