ADCS-ELM算法滚动轴承故障诊断  

Rolling bearing fault diagnosis based on ADCS-ELM algorithm

作　　者：余萍[1,2,3] 曹洁 黄开杰[1] YU Ping;CAO Jie;HUANG Kaijie(College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China;Key Laboratory of Advanced Control for Industrial Processes of Gansu Province,Lanzhou 730050,China;National Experimental Education Demonstration Center for Electrical and Control Engineering,Lanzhou University of Technology,Lanzhou 730050,China)

机构地区：[1]兰州理工大学电气工程与信息工程学院,甘肃兰州730050 [2]甘肃省工业过程控制重点实验室,甘肃兰州730050 [3]兰州理工大学电气与控制工程国家级实验教学示范中心,甘肃兰州730050

出　　处：《传感器与微系统》2020年第5期129-132,136,共5页Transducer and Microsystem Technologies

基　　金：国家自然科学基金资助项目(61763208);甘肃省自然科学基金资助项目(1506RJZA104)。

摘　　要：针对滚动轴承的故障信息难以从复杂噪声背景下的非平稳振动信号中提取且传统方法分类精度低等问题,提出一种基于集合经验模态分解(EEMD)能量特征提取和优化极限学习机神经网络(ADCS-ELM)分类诊断相结合的轴承故障诊断方法。利用EEMD对非线性和非平稳信号的自适应分解能力,将待检测轴承故障信号分解为包含故障特征的固有模态函数集(IMFs),并提取能量特征向量;利用自适应动态搜索步长改进布谷鸟搜索算法(ADCS)优化ELM网络连接权值和隐层阈值;将提取的故障特征向量用于训练极限学习机神经网络,得到最优权值和阈值;利用ADCS-ELM进行轴承故障诊断实验。实验结果表明:与BP,LVQ和ELM网络轴承故障诊断方法相比较,所提方法能够有效提高故障识别准确率,并且具有更快的计算速度。Aiming at the problem that the fault information of rolling bearings is difficult to extract from the non-stationary vibration signals from complex noise background,and the poor classification precision of traditional methods,a fault diagnosis method of rolling bearings based on energy feature extraction of ensemble empirical mode decomposition(EEMD)and classification diagnosis of an optimized extreme learning machine(ADCS-ELM)is proposed.Firstly,based on the adaptive decomposition ability of EEMD to nonlinear and non-stationary signals,the bearing fault signals are decomposed into an intrinsic mode function set(IMFs)which containing full of fault features,and the fault feature vectors are extracted.Then,the cuckoo search algorithm(ADCS),which is optimized by an adaptive dynamic search step strategy,is used to determine connection weights and the hidden layer threshold of the ELM.The optimal weights and thresholds can be obtained by training the ELM with the extracted fault feature vectors.Finally,the bearing fault diagnosis simulation experiments are carried out based on the ADCS-ELM to verify the effectiveness of the proposed method.Results show that,compared with the BP,LVQ and ELM methods,ADCS-ELM can improve the classification accuracy of the four typical bearing faults,and also have faster diagnosis speed.

关 键 词：集合经验模态分解 固有模态函数集 极限学习机 布谷鸟搜索算法 故障诊断 滚动轴承 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]