[1]周正南,刘 美,吴斌鑫,等.改进的布谷鸟算法优化极限学习机的石化轴承故障分类[J].机械与电子,2022,(07):3-7.
　ZHOU Zhengnan,LIU Mei,et al.Improved Cuckoo Algorithm for Optimizing Extreme Learning Machine for Petrochemical Bearing Fault Classification[J].Machinery & Electronics,2022,(07):3-7.
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改进的布谷鸟算法优化极限学习机的石化轴承故障分类()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2022年07期
页码:
3-7
栏目:
设计与研究
出版日期:
2022-07-28

文章信息/Info

Title:
Improved Cuckoo Algorithm for Optimizing Extreme Learning Machine for Petrochemical Bearing Fault Classification
文章编号:
1001-2257 ( 2022 ) 07-0003-05
作者:
周正南 1 ; 2 ; 刘 美 1 ; 吴斌鑫 1 ;  2 ; 高兴泉 2 ; 张 斐 3
1. 吉林化工学院,吉林 吉林 132022 ; 2. 广东石油化工学院,广东 茂名 525000 ;3. 东莞理工学院,广东 东莞 523419
Author(s):
ZHOU Zhengnan1 ;  2 ;  LIU Mei1 ;  WU Binxin1 ;  2 ;  GAO Xingquan2 ;  ZHANG Fei3
( 1.Jilin Institute of Chemical Technology , Jilin 132022 , China ; 2.Guangdong University of Petrochemical Technology , Maoming 525000 , China ; 3.Dongguan University of Technology , Dongguan 523419 , China )
关键词:
滚动轴承; 故障诊断; 布谷鸟算法; 极限学习机
Keywords:
rolling bearings ;  fault diagnosis ;  cuckoo algorithm ;  extreme learning machine
分类号:
TP206.3 ; TH133.3
文献标志码:
A
摘要:
针对通用的智能故障诊断方法在石化滚动轴承中准确率不理想的问题,提出一种通过改进的布谷鸟算法( CS )优化极限学习机( ELM )使诊断准确率提高的模型。将实测轴承振动信号降噪处理,计算不同嵌入维度下的关联维数作为 ELM 的输入信号;通过改进的布谷鸟算法获取极限学习机最优的隐含层偏置、输入权重,最后输出诊断结果。经过实验证明,该方法可以有效地克服测量信号时的干扰,可以对不同故障下的滚动轴承准确识别,并与多种模型对比,该方法的故障诊断准确率为 97.5% 。
Abstract:
Aiming at the problem of unsatisfactory accuracy of the general intelligent fault diagnosis method in petrochemical rolling bearings , a model is proposed to optimize the limit learning machine ( ELM ) by the improved cuckoo intelligent optimization algorithm ( CS ) to make the diagnosis accuracy improved.The measured bearing vibration signal is processed by noise reduction , and the correlation dimensions under different embedding dimensions are calculated as features as the input signal of ELM ; the optimal implied layer bias and input weight output diagnosis results of the limit learning machine are obtained by the improved cuckoo algorithm.The experimental results prove that the method proposed in this paper can effectively overcome the interference when measuring the signal , and can accurately identify the rolling bearing under different faults , and compared with a variety of models , the fault diagnosis accuracy of the method proposed is 97.5%.

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备注/Memo

备注/Memo:
收稿日期: 2022-02-11
基金项目:国家自然科学基金面上项目( 62073091 );广东省高校重点领域(新一代信息技术)专项( 2020ZDZX3042 );东莞理工学院机器人与智能装备创新中心项目( KCYCXPT2017006 );广东省普通高校机器人与智能装备重点实验室项目( 2017KSYS009 );广东省普通高校特色创新项目( 2017KTSCX176 );湖南省重点实验室开放基金项目( 21903 )
作者简介:周正南 ( 1997- ),男,吉林辽源人,硕士研究生,研究方向为石化机组轴承故障诊断;刘 美 ( 1967- ),女,广东湛江人,工学博士,教授,研究方向为智能检测与智能控制,通信作者。
更新日期/Last Update: 2022-08-24