基于VMD消噪處理的滾動軸承早期故障識別
發(fā)布時間：2018-11-12 07:36
【摘要】：提出了一種基于變分模態(tài)分解(VMD)消噪和核模糊C均值(KFCM)聚類相結(jié)合的滾動軸承早期故障識別方法。首先提出一種通過綜合運(yùn)用泄漏能量和互相關(guān)系數(shù)函數(shù)確定VMD預(yù)設(shè)尺度數(shù)K的新方法,彌補(bǔ)了VMD方法通常按經(jīng)驗(yàn)選取預(yù)設(shè)尺度數(shù)方法的不足;然后對振動信號進(jìn)行VMD分解得到K個限帶的內(nèi)稟模態(tài)函數(shù)(BIMF)分量,利用歸一化的自相關(guān)系數(shù)函數(shù)能量集中比大于0.9的原則確定含有噪聲的BIMF分量,并剔除這些含噪BIMF分量,再將剩余的BIMF分量疊加進(jìn)行信號重構(gòu),實(shí)現(xiàn)了信號的消噪;最后計算各樣本重構(gòu)信號的均方根值和歸一化能量值得到二維特征向量樣本集,并輸入到KFCM聚類器進(jìn)行故障診斷。利用實(shí)測軸承故障數(shù)據(jù)進(jìn)行驗(yàn)證,結(jié)果表明與經(jīng)驗(yàn)?zāi)B(tài)分解(EMD)方法相比,可以有效地實(shí)現(xiàn)滾動軸承早期故障診斷。
[Abstract]:This paper presents an early fault identification method for rolling bearings based on variational mode decomposition (VMD) (VMD) de-noising and kernel fuzzy C-means (KFCM) clustering. Firstly, a new method is proposed to determine the preset scale number K of VMD by synthetically using leakage energy and correlation number function, which makes up for the deficiency of VMD method which usually selects preset scale number according to experience. Then the intrinsic mode function (BIMF) component of K band is obtained by VMD decomposition of the vibration signal, and the BIMF component with noise is determined by the principle of normalized autocorrelation coefficient function energy concentration ratio greater than 0.9. These noisy BIMF components are eliminated, and then the remaining BIMF components are superimposed to reconstruct the signal to realize signal de-noising. Finally, the mean square root value and normalized energy value of each sample reconstruction signal are calculated to obtain the two-dimensional eigenvector sample set, and input into the KFCM cluster for fault diagnosis. Compared with the empirical mode decomposition (EMD) method, the early fault diagnosis of rolling bearing can be realized effectively.
【作者單位】： 燕山大學(xué)河北省重型機(jī)械流體動力傳輸與控制重點(diǎn)實(shí)驗(yàn)室;燕山大學(xué)先進(jìn)鍛壓成形技術(shù)與科學(xué)教育部重點(diǎn)實(shí)驗(yàn)室;鄭州中車四方軌道車輛有限公司;
【基金】：國家自然科學(xué)基金(51475405) 國家重點(diǎn)基礎(chǔ)研究發(fā)展計劃(973計劃)(2014CB046405) 秦皇島市科學(xué)技術(shù)研究與發(fā)展計劃(201502A041)
【分類號】：TH133.33
本文編號：2326488
[Abstract]:This paper presents an early fault identification method for rolling bearings based on variational mode decomposition (VMD) (VMD) de-noising and kernel fuzzy C-means (KFCM) clustering. Firstly, a new method is proposed to determine the preset scale number K of VMD by synthetically using leakage energy and correlation number function, which makes up for the deficiency of VMD method which usually selects preset scale number according to experience. Then the intrinsic mode function (BIMF) component of K band is obtained by VMD decomposition of the vibration signal, and the BIMF component with noise is determined by the principle of normalized autocorrelation coefficient function energy concentration ratio greater than 0.9. These noisy BIMF components are eliminated, and then the remaining BIMF components are superimposed to reconstruct the signal to realize signal de-noising. Finally, the mean square root value and normalized energy value of each sample reconstruction signal are calculated to obtain the two-dimensional eigenvector sample set, and input into the KFCM cluster for fault diagnosis. Compared with the empirical mode decomposition (EMD) method, the early fault diagnosis of rolling bearing can be realized effectively.
【作者單位】： 燕山大學(xué)河北省重型機(jī)械流體動力傳輸與控制重點(diǎn)實(shí)驗(yàn)室;燕山大學(xué)先進(jìn)鍛壓成形技術(shù)與科學(xué)教育部重點(diǎn)實(shí)驗(yàn)室;鄭州中車四方軌道車輛有限公司;
【基金】：國家自然科學(xué)基金(51475405) 國家重點(diǎn)基礎(chǔ)研究發(fā)展計劃(973計劃)(2014CB046405) 秦皇島市科學(xué)技術(shù)研究與發(fā)展計劃(201502A041)
【分類號】：TH133.33
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1 李瀛寰;VMD聯(lián)盟加速移動多媒體產(chǎn)業(yè)鏈[N];中國計算機(jī)報;2004年
2 沈蓓;VMD技術(shù)拓寬企業(yè)自主創(chuàng)新之路[N];人民郵電;2005年
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