本文選題：變分模態(tài)分解 + 平穩(wěn)小波　； 參考：《機(jī)械設(shè)計(jì)與研究》2017年01期

【摘要】：為了從強(qiáng)噪聲背景下的軸承振動信號中準(zhǔn)確穩(wěn)定地提取滾動軸承故障特征,提出了基于峭度準(zhǔn)則VMD及平穩(wěn)小波的軸承故障診斷方法。使用變分模態(tài)分解對同一負(fù)荷下的故障信號進(jìn)行預(yù)處理,通過峭度準(zhǔn)則篩選出最佳和次佳信號分量進(jìn)行重構(gòu)并使用平穩(wěn)小波進(jìn)行去噪處理,最后分析信號的包絡(luò)譜來對軸承的故障類型進(jìn)行判斷。通過對仿真滾動軸承內(nèi)圈故障信號進(jìn)行分析,該方法可成功提取出微弱特征頻率信息,噪聲抑制效果優(yōu)于EMD。由此表明,基于峭度準(zhǔn)則VMD及平穩(wěn)小波的軸承故障診斷可有效提取強(qiáng)聲背景下的滾動軸承早期故障信息,具有一定的可靠性和應(yīng)用價值。
[Abstract]:In order to extract the fault features of rolling bearing accurately and stably from the vibration signal of bearing under strong noise, a bearing fault diagnosis method based on kurtosis criterion VMD and stationary wavelet is proposed. The variational mode decomposition is used to preprocess the fault signal under the same load. The best and sub-optimal signal components are selected by kurtosis criterion and the stationary wavelet is used to Denoise the fault signal. Finally, the envelope spectrum of the signal is analyzed to judge the fault type of bearing. By analyzing the fault signal of the inner ring of rolling bearing, the method can extract the weak characteristic frequency information successfully, and the noise suppression effect is better than that of EMD. It is shown that the bearing fault diagnosis based on kurtosis criterion VMD and stationary wavelet can effectively extract the early fault information of rolling bearing under strong sound background and has certain reliability and application value.
【作者單位】： 蘭州交通大學(xué)機(jī)電工程學(xué)院;
【分類號】：TH133.3

【參考文獻(xiàn)】

中國期刊全文數(shù)據(jù)庫 前8條

1 韓中合;徐搏超;朱霄s，

本文編號：2050634