摘要
针对滚动轴承在复杂背景下难以实现信号降噪并进行故障诊断的问题,提出了一种基于改进的鹈鹕优化算法(IPOA)优化变分模态分解(VMD)和神经网络的故障诊断模型。首先,为了提高算法的效率,引入自适应惯性权重和非线性收敛因子对鹈鹕优化算法进行改进和优化,利用改进后的算法优化得到VMD的关键参数K和α;其次,使用得到的参数对原始信号进行VMD分解,对得到的K个IMF进行时频域的特征提取;最后,利用CNN-BiLSTM网络对滚动轴承的不同状态进行识别。滚动轴承数据集试验表明,采用IPOA-VMD方法提取的特征数据,相较于使用POA-VMD,PSO-VMD,VMD方法提取的特征数据,经过CNN-BiLSTM网络进行故障诊断时,在大基数的条件下,准确率仍有所提升,在测试集数据集上的平均准确率达到了99.867%。
In order to address the challenge of signal denoising and fault diagnosis for rolling bearings in complex environments,a fault diagnosis model based on an improved pelican optimization algorithm(IPOA)combined with variational mode decomposition(VMD)and neural networks was proposed.To enhance the algorithm efficiency,adaptive inertia weights and nonlinear convergence factors were first introduced to improve and optimize the pelican optimization algorithm.The improved algorithm was then used to optimize and determine the critical parameters K andαof VMD.Subsequently,the optimized parameters were applied for VMD decomposition of the original signal,and time-frequency domain features were extracted from the resulting K intrinsic mode functions(IMFs).Finally,a CNN-BiLSTM network was employed to classify the different states of rolling bearings.Experimental results on a rolling bearing dataset demonstrate that the feature data extracted using the IPOA-VMD method achieves higher fault diagnosis accuracy compared to feature data extracted using POA-VMD,PSO-VMD and VMD methods.On the test dataset,the proposed method achieves an average diagnostic accuracy of 99.867%with a large sample size.
作者
阎羿凡
李英顺
佟维妍
于浚豪
YAN Yifan;LI Yingshun;TONG Weiyan;YU Junhao(School of Chemical Process Automation,Shenyang University of Technology,Liaoyang 111003,China;School of Control Science and Engineering,Dalian University of Technology,Dalian 116200,China;Shenyang Shunyi Technology Co.,Ltd.,Shenyang 110000,China)
出处
《车用发动机》
北大核心
2025年第4期87-94,共8页
Vehicle Engine
基金
辽宁省科学技术计划项目“面向航空发动机运行维护的健康管理数字仿真系统研发与应用”(2022JH1/10400007)。
关键词
滚动轴承
鹈鹕优化算法
变分模态分解
神经网络模型
故障诊断
rolling bearing
pelican optimization algorithm
variational mode decomposition
neural network model
fault diagnosis
