摘要
针对深度神经网络用于滚动轴承故障诊断时,网络隐含层层数、各隐含层节点数、稀疏系数以及输入数据置零比例等超参数会直接影响网络诊断性能的问题,提出了一种优化改进堆叠降噪自编码器(SDAE)的滚动轴承故障智能诊断方法。使用学生心理优化算法(SPBO)对降噪自编码器(DAE)网络的超参数进行自适应选取来确定SDAE网络的最优结构和参数,据此提取具有更强表征力的故障状态特征表示,输入到soft-max分类器实现滚动轴承运行工况的精确诊断。使用3个开源数据集对所提网络的性能进行验证,实验结果表明,基于SPBO-SDAE网络的诊断方法在特征有效提取、诊断速度以及故障诊断准确率方面均优于支持向量机(SVM)、反向传播(BP)神经网络、径向基(RBF)神经网络、SDAE网络、SPBO优化后的深度置信网络(DBN)、遗传算法(GA)优化后的SDAE网络以及粒子群算法(PSO)优化后的SDAE网络。
In view of the problem that hyper parameters such as the number of hidden layers,the number of nodes in each hidden layer,the sparse coefficient and the dropout ratio of input data will directly affect the diagnosis performance of the network when deep neural network is used for rolling bearing fault diagnosis,an intelligent fault diagnosis method of rolling bearing based on optimized and improved stacked denoising auto encoders(SDAE)was proposed.The student psychology based optimization(SPBO)algorithm was used to adaptively select the hyper parameters of the denoising auto encoder(DAE)network to determine the optimal structure and parameters of SDAE network.Then the fault state features with stronger representation was extracted and input to soft-max classifier to achieve accurate diagnosis of rolling bearing operating conditions.Three open source datasets are used to verify the performance of the proposed network,the results illustrate that the diagnosis method based on SPBO-SDAE network is superior to support vector machine(SVM),back propagation(BP)neural network,radial basis function(RBF)neural network,traditional SDAE network,SPBO based deep belief network(DBN),genetic algorithm(GA)based SDAE network and particle swarm optimization(PSO)based SDAE network in feature extraction,diagnosis speed and fault diagnosis accuracy.
作者
杜先君
贾亮亮
Xian-jun DU;Liang-liang JIA(College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China;Key Laboratory of Gansu Advanced Control for Industrial Processes,Lanzhou University of Technology,Lanzhou 730050,China;National Demonstration Center for Experimental Electrical and Control Engineering Education,Lanzhou University of Technology,Lanzhou 730050,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2022年第12期2827-2838,共12页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(61963025)
甘肃省高等学校创新基金项目(2021A-027).
关键词
故障诊断
滚动轴承
堆叠降噪自编码器
超参优化
特征提取
fault diagnosis
rolling bearing
stacked denoising auto encoders
hyperparametric optimization
feature extraction
