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  • 中国标准连:ISSN1005-2895
  • 续出版物号: CN 33-1180/TH
  • 主管单位:轻工业杭州机电设计研究院有限公司
  • 主办单位:轻工业杭州机电设计研究院有限公司、中国轻工机械协会、中国轻工业机械总公司
  • 社  长:刘安江
  • 主  编:黄丽珍
  • 地  址:杭州市余杭区高教路970号西溪联合科技广场4-711
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刘庆友, 娄志宁, 赵新维*.基于K SVD联合参数自适应TQWT的齿轮箱故障诊断[J].轻工机械,2024,42(6):65-72
基于K SVD联合参数自适应TQWT的齿轮箱故障诊断
Gearbox Fault Diagnosis Based on K SVD Joint Parameter Adaptive TQWT
  
DOI:10.3969/j.issn.1005 2895.2024.06.009
中文关键词:  故障诊断  齿轮箱  K 均值奇异值分解  可调品质因子小波变换  自相关峭谱积
英文关键词:fault diagnosis  gearbox  K SVD(K means Singular Value Decomposition)  TQWT(Tunable Q factor Wavelet Transform)  autocorrelation kurtosis
基金项目:
作者单位
刘庆友, 娄志宁, 赵新维* 江南大学 机械工程学院 江苏 无锡214122 
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中文摘要:
      针对齿轮箱信号在强噪声背景下故障特征提取难的问题,课题组提出了一种K 均值奇异值分解(K means singular value decomposition,K SVD)联合参数自适应可调品质因子小波变换(tunable Q factor wavelet transform,TQWT)的齿轮箱故障诊断方法。利用K SVD稀疏表示齿轮箱故障信号,重构信号后去除噪声;针对TQWT对噪声鲁棒性不强且参数过度依赖人为选择的问题,结合齿轮箱早期故障信号的冲击性与周期性特征,提出了自相关峭谱积指标,以自相关峭谱积为优化指标对TQWT参数进行自适应选择;根据自相关峭谱积指标对子带进行筛选,对选出的子带进行重构,通过包络谱分析得到齿轮箱故障特征信息。仿真和试验结果表明所提出的诊断方法能有效提取低转速、强噪声背景下的齿轮箱故障特征。
英文摘要:
      A gear box fault diagnosis method based on K means singular value decomposition (K SVD) combined with parameter adaptive tunable Q factor wavelet transform (TQWT) is proposed to address the difficulty of extracting fault features from gear box signals in strong noise backgrounds. Using K SVD to sparsely represent gearbox fault signals, reconstructing the signals and removing noise, in response to the problem of weak noise robustness and excessive reliance on manual parameter selection in TQWT, combined with the impact and periodic characteristics of early gearbox fault signals, an autocorrelation kurtosis product index is proposed. The autocorrelation kurtosis product is used as the optimization index to adaptively select TQWT parameters. Based on the autocorrelation kurtosis product index, the selected sub bands are screened, reconstructed, and the fault feature information of the gearbox is obtained through envelope spectrum analysis. The simulation and experimental results show that the proposed diagnostic method can effectively extract the fault characteristics of gearbox under low speed and strong noise background.
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