基于自适应优化的TQWT轴承早期故障诊断方法
|
摘要
为了准确提取滚动轴承早期微弱故障特征,提出基于自适应优化的可调品质因子小波变换(tunable Q-factor wavelet transform,TQWT)轴承早期故障诊断方法。该方法利用包络谱特征频率强度系数的参数自适应寻优方法来自适应优化TQWT,以弥补传统TQWT参数选择过分依赖人工经验的不足。首先利用该方法获得振动信号的最优分解结果,然后通过分析最优分解结果的包络谱来判断轴承故障类型。通过分析仿真信号以及工程试验数据证明了该方法的有效性。
In order to extract accurately the early weak fault characteristics of rolling bearings,a bearing early fault diagnosis method based on adaptive optimization tunable Q-factor wavelet transform( TQWT) is proposed. In this method,the TQWT method is optimized based on envelope spectrum characteristic frequency intensity coefficient to compensate for the shortage of traditional TQWT parameter selection,which relies too much on artificial experience. First,the optimal decomposition results of the vibration signal are obtained by this method,and then the type of the bearing fault is judged by the analysis of the envelope spectrum of the optimal decomposition results. The effectiveness of the method described in this paper is proved by the analysis of the simulation signal and the engineering test data.
In order to extract accurately the early weak fault characteristics of rolling bearings,a bearing early fault diagnosis method based on adaptive optimization tunable Q-factor wavelet transform( TQWT) is proposed. In this method,the TQWT method is optimized based on envelope spectrum characteristic frequency intensity coefficient to compensate for the shortage of traditional TQWT parameter selection,which relies too much on artificial experience. First,the optimal decomposition results of the vibration signal are obtained by this method,and then the type of the bearing fault is judged by the analysis of the envelope spectrum of the optimal decomposition results. The effectiveness of the method described in this paper is proved by the analysis of the simulation signal and the engineering test data.
引文
[1]李志星,石博强.自适应奇异值分解的随机共振提取微弱故障特征[J].农业工程学报,2017,33(11):60-67.
[2]任学平,王朝阁,张玉皓,等.基于双树复小波包自适应Teager能量谱的滚动轴承早期故障诊断[J].振动与冲击,2017,36(10):84-92.
[3]王建国,陈帅,张超.噪声参数最优ELMD与LS-SVM在轴承故障诊断中的应用与研究[J].振动与冲击,2017,36(5):72-78.
[4]叶昊,王桂增,方崇智.小波变换在故障检测中的应用[J].自动化学报,1997,23(6):736-741.
[5]唐贵基,王晓龙.可调品质因子小波变换在滚动轴承微弱故障特征提取中的应用[J].中国电机工程学报,2016,36(3):746-754.
[6]余发军,周凤星.基于可调Q因子小波变换和谱峭度的轴承早期故障诊断方法[J].中南大学学报:自然科学版,2015(11):4122-4128.
[7]Selesnick I W. Wavelet transform with tunable Q-factor[J]. IEEETransactions on Signal Processing,2011,59(8):3560-3575.
[8]桑迎平,蔡晋辉,曾九孙,等.基于形态优化滤波的轴承故障特征提取方法[J].电子测量与仪器学报,2016,30(5):757-763.
[9]Torres M E,Colominas M A,Schlotthauer G,et al. A complete ensemble empirical mode decomposition with adaptive noise[C]//IEEE International Conference on Acoustics,Speech and Signal Processing. IEEE,2011:4144-4147.
[10]唐贵基,王晓龙.自适应最大相关峭度解卷积方法及其在轴承早期故障诊断中的应用[J].中国电机工程学报,2015,35(6):1436-1444.
[2]任学平,王朝阁,张玉皓,等.基于双树复小波包自适应Teager能量谱的滚动轴承早期故障诊断[J].振动与冲击,2017,36(10):84-92.
[3]王建国,陈帅,张超.噪声参数最优ELMD与LS-SVM在轴承故障诊断中的应用与研究[J].振动与冲击,2017,36(5):72-78.
[4]叶昊,王桂增,方崇智.小波变换在故障检测中的应用[J].自动化学报,1997,23(6):736-741.
[5]唐贵基,王晓龙.可调品质因子小波变换在滚动轴承微弱故障特征提取中的应用[J].中国电机工程学报,2016,36(3):746-754.
[6]余发军,周凤星.基于可调Q因子小波变换和谱峭度的轴承早期故障诊断方法[J].中南大学学报:自然科学版,2015(11):4122-4128.
[7]Selesnick I W. Wavelet transform with tunable Q-factor[J]. IEEETransactions on Signal Processing,2011,59(8):3560-3575.
[8]桑迎平,蔡晋辉,曾九孙,等.基于形态优化滤波的轴承故障特征提取方法[J].电子测量与仪器学报,2016,30(5):757-763.
[9]Torres M E,Colominas M A,Schlotthauer G,et al. A complete ensemble empirical mode decomposition with adaptive noise[C]//IEEE International Conference on Acoustics,Speech and Signal Processing. IEEE,2011:4144-4147.
[10]唐贵基,王晓龙.自适应最大相关峭度解卷积方法及其在轴承早期故障诊断中的应用[J].中国电机工程学报,2015,35(6):1436-1444.