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滚动轴承优选WPE与ANVTPSO-BPNN故障诊断 被引量:1

Fault Diagnosis of Rolling Bearing Using Optimal WPE-Based ANVTPSO-BPNN
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摘要 为了提高滚动轴承故障诊断的效率和准确率,提出一种基于优选小波包能量(wavelet packet energy,简称WPE)联合自适应无速度项粒子群优化前馈神经网络(adaptive no velocity term particle swarm optimization-back propagation neural network,简称ANVTPSO-BPNN)的滚动轴承故障诊断方法。首先,采用小波分析对轴承振动信号进行消噪,并通过小波包分解提取能量特征,对基函数和分解层数进行优选;其次,采用自适应方式调节PSO算法的惯性权重和学习因子,并对标准PSO算法舍弃速度项以避免粒子初始速度对算法收敛速度和求解精度的影响;最后,针对某滚动轴承的实测数据,完成了5种不同策略的BPNN算法验证。结果表明:提出的方法迭代步数只有273步,诊断精度达到99%,较消噪前后的BPNN及消噪后的2种PSO-BPNN,具有更高的诊断效率和准确率。 In order to improve the efficiency and accuracy of fault diagnosis,a fault diagnosis method of rolling bearing of the adaptive no velocity term particle swarm optimization-back propagation neural network(ANVTP-SO-BPNN)with optimal wavelet packet energy(WPE)is proposed.The wavelet analysis is used to denoise the vibration signal,and the energy feature is extracted by the wavelet packet decomposition.The basis function and decomposition level are optimized.The inertia weight and learning factor of particle swarm optimization(PSO)algorithm are adaptively adjusted,and the speed term of standard PSO is discarded to avoid the influ-625 Journal of Vibration,Measurement&Diagnosis Vol.43 ence of particle initial velocity on the convergence speed and solution accuracy.According to the measured data of a certain bearing,five different BPNN algorithms are verified.The results show that the proposed method has only 273 iteration steps and its diagnosis accuracy reaches 99%.Compared with BPNN before and after noise elimination and two PSO-BPNNs after noise elimination,the method has the higher diagnosis efficiency and ac-curacy.
作者 樊红卫 严杨 张旭辉 张超 曹现刚 薛策译 毛清华 李杰 FAN Hongwei;YAN Yang;ZHANG Xuhui;ZHANG Chao;CAO Xiangang;XUE Ceyi;MAO Qinghua;LI Jie(School of Mechanical Engineering,Xi'an University of Science and Technology Xi'an,710054,China;Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control,Xi'an University of Science and Technology Xi'an,710054,China)
出处 《振动.测试与诊断》 EI CSCD 北大核心 2023年第3期593-602,625,626,共12页 Journal of Vibration,Measurement & Diagnosis
基金 国家自然科学基金资助项目(51605380,51974228,52275131) 陕西省自然科学基础研究计划资助项目(2019JLZ-08) 陕西省大学生创新创业训练计划资助项目(S202010704059)。
关键词 滚动轴承 故障诊断 小波消噪 小波包分解 粒子群优化 神经网络 rolling bearing fault diagnosis wavelet denoising wavelet packet decomposition particle swarm optimization neural network
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