摘要
针对滚动轴承的振动信号易受到噪声干扰、故障特征提取困难、诊断精度低的问题,提出一种基于DE优化VMD与CNN-BiGRU-Attention结合的模型进行故障识别与诊断的方法。根据最小化包络熵原则,用DE优化VMD,得到最佳分解层数和惩罚因子,计算最佳分解参数组合下的各个IMF的相关系数,根据设定的阈值筛选有用信号完成信号重构。首先将识别模型与常用分类模型进行对比了实验,本文所提方法的accuracy、precision、recall和F1 score均高于其他方法,故障诊断准确率达到了99.17%。其次将原始信号和重构信号特征输入CNN-BiGRU-Attention模型中,原始信号在本文所提模型的故障准确率为89.58%,低于信号降噪后的准确率。最后将CNN-BiGRU-Attention与CNN-BiLSTM-Attention模型对比,CNN-BiGRU-Attention比CNN-BiLSTM-Attention模型准确率高1.25%、GPU占用率降低21%、CPU占用率降低23%,训练时间快35s,实验研究结果可为现有滚动轴承故障诊断技术提供一种有效的改进方法。
To address the challenges of noise interference,difficult fault feature extraction,and low diagnostic accuracy in rolling bearing vibration signals,this study proposes a fault diagnosis method based on differential evolution(DE)-optimized variational mode decomposition(VMD)combined with a comprehensive model integrating convolutional neural networks(CNN)-bidirectional gated recurrent unit(BiGRU)-Attention.Following the minimum envelope entropy principle,DE is employed to optimize VMD,obtaining the optimal decomposition layer number and penalty factor.The correlation coefficients of intrinsic mode functions(IMFs)under the optimal parameter combination are calculated,and useful signals are reconstructed based on a predetermined threshold.Comparative experiments with conventional classification models demonstrate that the proposed method achieves superior performance in accuracy,precision,recall and F1 score,with a fault diagnosis accuracy of 99.17%.When comparing the diagnostic results between raw and reconstructed signals in the CNN-BiGRU-Attention model,the accuracy for raw signals is 89.58%,lower than that of the denoised signals.Finally,CNN-BiGRU-Attention was compared with the CNN-bidirectional long short-term memory(BiLSTM)-Attention model.The CNN-BiGRU-Attention model showed a 1.25%higher accuracy,a 21%reduction in graphic processing unit(GPU)usage,a 23%reduction in central processing unit(CPU)usage,and a training time that was 35 seconds faster.These experimental results can provide an effective improvement method for existing rolling bearing fault diagnosis technologies.
作者
邵鲁川
赵兵
康旭涛
Shao Luchuan;Zhao Bing;Kang Xutao(School of Mechanical Engineering,Qinghai University,Xining 810016,China)
出处
《电子测量技术》
北大核心
2026年第3期34-43,共10页
Electronic Measurement Technology
基金
青海省2025年重点研发与转化计划项目(2025-QY-209)资助。
关键词
滚动轴承
信号降噪
变分模态分解
诊断模型
故障诊断
rolling bearing
signal denoising
variational mode decomposition
diagnostic model
fault diagnosis
作者简介
邵鲁川,硕士研究生,主要研究方向为信号处理和故障诊断。E-mail:luchuanshao@qhu.edu.cn;通信作者:赵兵,博士,副教授,主要研究方向为装配工艺基础理论、数字化与智能化制造相关研究工作。E-mail:zzipwe@126.com;康旭涛,硕士研究生,主要研究方向为深度学习和故障诊断。E-mail:kxt121412@163.com。
