首页  |  期刊简介  |  期刊荣誉  |  编委会  |  现任主编  |  投稿指南  |  下载中心  |  期刊征订
中文核心期刊
Ei Compendex收录期刊
中国科学引文数据库来源期刊
中文科技期刊数据库收录期刊
国际刊号:1004-6801
国内刊号:32-1361/V
用户登录
  E-mail:  
  密  码:  
  作者 审稿  
  编辑 读者  
期刊向导
联系方式
  • 主管:中华人民共和国工业和信息化部
  • 主办:南京航空航天大学
  •           全国高校机械工程测试技术研究会
  • 国际刊号:1004-6801
  • 国内刊号:32-1361/V
  • 地址:南京市御道街29号
  • 电话:025-8489 3332
  • 传真:025-8489 3332
  • E-mail:qchen@nuaa.edu.cn
  • 邮编:210016
基于IVMD与改进KELM的发动机故障诊断
Engine Fault Diagnosis Based on Independent Variational Mode Decomposition and Improved Kernel Extreme Learning Machine
  
DOI:10.16450/j.cnki.issn.1004-6801.2019.04.028
中文关键词:  故障诊断  核极限学习机  社会情感优化算法  频谱循环相干系数  独立变分模态分解
英文关键词:fault diagnosis  kernel extreme learning machine  social emotional optimization algorithm  spectral cyclic coherence coefficient  independent variational mode decomposition
基金项目:国家自然科学基金资助项目(51305454)
作者单位
刘敏,张英堂,李志宁,范红波 (陆军工程大学石家庄校区七系 石家庄050003) 
摘要点击次数: 278
全文下载次数: 5218
中文摘要:
      为从含有较强噪声的缸盖振动信号中提取有效的故障特征并进行故障分类,提出了采用独立变分模态分解(independent variational mode decomposition,简称IVMD)与改进核极限学习机(improved kernel extreme learning machine,简称IKELM)的发动机故障诊断方法。首先,根据频谱循环相干系数选取匹配波形对信号进行端点延拓,并利用变分模态分解(variational mode decomposition,简称VMD)将延拓后信号分解为一系列固有模态分量,有效抑制了VMD中的端点效应;其次,选取有效分量作为输入观测信号,进行核独立成分分析,进一步分离干扰噪声与有效信号,并消除模态混叠,得到相互独立的有效故障特征频带,进而提取各频带的自回归模型参数、多尺度模糊熵和标准化能量矩构建故障特征向量集;最后,建立基于社会情感优化算法的IKELM分类模型,对故障特征进行分类,实现发动机故障诊断。仿真和实验结果表明,所提出的方法可有效抑制VMD的端点效应,提高信号分解精度,消除噪声干扰并分离出相互独立的有效故障特征频带,增强特征参数辨识度,最终提高发动机故障诊断速度与精度,发动机故障诊断平均准确率达到99.85%。
英文摘要:
      In order to extract effective fault features from the cylinder head vibration signal under strong noise background and classify faults, an engine fault diagnosis method of independent variational mode decomposition (IVMD) combined with improved kernel extreme learning machine is proposed. Firstly, the matching waveform is selected to carry out the end extension of the original signal according to the spectral cyclic coherence coefficient. And the signal after end extension is decomposed into a number of intrinsic mode functions (IMFs) by using variational mode decomposition (VMD). The end effect in VMD is suppressed effectively. Then the selected effective IMFs and original signal are constructed as the input observation signals of kernel independent component analysis (KICA). After KICA, the noise and effective signal are separated further, the mode mixing is eliminated and the independent effective fault feature bands are obtained. Autoregressive model parameters, multi-scale fuzzy entropy and normalized energy moment of each frequency band are extracted to construct a joint fault feature vector. Lastly the improved KELM model based on social emotional optimization algorithm (SEOA-KELM) is constructed to classify the fault features in order to realize the engine fault diagnosis. The simulation and experimental results show that the proposed method can effectively suppress the end effect in VMD, improve the signal decomposition precision, eliminate the noise,separate independent and effective fault feature frequency bands, enhance feature parameter identification and improve the speed and accuracy of engine fault diagnosis finally. The average accuracy rate of engine fault diagnosis is up to 99.85%.
查看全文  查看/发表评论  下载PDF阅读器
关闭

Copyright @2010-2015《振动、测试与诊断》

地址:南京市御道街29号        邮编:210016

电话:025-8489 3332      传真:025-8489 3332       E-mail:qchen@nuaa.edu.cn

您是本站第2218349位访问者 本站今日一共被访问465

技术支持:北京勤云科技发展有限公司