自适应可调品质因子小波变换在轴承早期故障诊断中的应用

王晓龙; 唐贵基; 周福成

doi:10.13224/j.cnki.jasp.2017.10.020

自适应可调品质因子小波变换在轴承早期故障诊断中的应用

doi: 10.13224/j.cnki.jasp.2017.10.020

华北电力大学能源动力与机械工程学院,河北保定 071003

基金项目: 国家自然科学基金(51777074); 中央高校基本科研业务费专项资金(2015XS120，2014MS154)

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出版历程
- 收稿日期: 2016-03-19
- 刊出日期: 2017-10-28

Application of adaptive tunable Qfactor wavelet transform on incipient fault diagnosis of bearing

摘要

摘要: 分析了可调品质因子小波变换(TQWT)的近似平移不变性,并通过模拟信号对该性质进行验证。提出了基于时频峭度指标优化的自适应可调品质因子小波变换(ATQWT)方法,用于解决滚动轴承早期故障诊断问题。首先利用时频峭度指标对TQWT的品质因子和冗余因子进行搜寻,确定最优影响参数后,根据所得结果设置好TQWT的参数并对原始信号进行处理,得到相应的信号分量并选定最佳信号分量,对最佳信号分量执行包络解调处理,最后分析包络谱中的频率成分来判定轴承的状态。实验信号分析结果表明:所得时频峭度指标更加可靠，鲁棒性更强。在低信噪比情况下,该方法可以准确分离出原始信号中的微弱特征,有效判定轴承的早期故障。
- 滚动轴承 /
- 早期故障 /
- 可调品质因子 /
- 小波变换 /
- 时频峭度指标
Abstract: The shift invariant characteristic of tunable Qfactor wavelet transform (TQWT) was analyzed, and verified through simulated signal. A method named adaptive tunable Qfactor wavelet transform (ATQWT) based on timefrequency kurtosis index optimization was proposed to solve the problem of incipient fault diagnosis of rolling bearing. Firstly, the timefrequency kurtosis index was used to search for the quality factor and the redundancy factor of TQWT； after the optimal influencing parameters were confirmed, the parameters of TQWT were set according to the obtained results and the original signal was processed, then the corresponding signal components could be acquired and the optimal signal component could be confirmed. The envelope demodulation process was performed on the optimal signal component. Finally, the condition of the bearing could be judged by analyzing the frequency components of the envelope spectrum. The analysis results of the experiment signals show the timefrequency kurtosis index by the proposed method is more reliable, and robustrness is better. This method could accurately separate the weak feature from the original signal at low signal to noise ratio, and effectively judge the incipient fault of bearing.
- rolling bearing /
- incipient fault /
- tunable Qfactor /
- wavelet transform /
- timefrequency kurtosis index

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