本文選題：滾動軸承　切入點(diǎn)：總體局部均值分解　出處：《機(jī)械科學(xué)與技術(shù)》2017年11期

【摘要】：針對滾動軸承早期故障特征信號微弱且受環(huán)境噪聲影響嚴(yán)重,故障特征信息難以識別的問題,提出了基于總體局部均值分解(Ensemble local mean decomposition,ELMD)和最大相關(guān)峭度反褶積(Maximum correlated kurtosis deconvolution,MCKD)的早期故障診斷方法。該方法首先運(yùn)用ELMD對采集到的振動信號進(jìn)行分解,得到有限個乘積函數(shù)(Product function,PF),由于噪聲的干擾,從PF分量的頻譜中很難對故障做出正確的判斷。然后對包含故障特征的PF分量進(jìn)行最大相關(guān)峭度反褶積處理以消除噪聲影響,凸現(xiàn)故障特征信息。最后對降噪信號進(jìn)行Hilbert包絡(luò)譜分析,即可從中準(zhǔn)確地識別出軸承的故障特征頻率。通過軸承故障模擬實(shí)驗(yàn)和工程應(yīng)用實(shí)例驗(yàn)證了該方法的有效性與優(yōu)越性。
[Abstract]:Aiming at the problem that the early fault characteristic signal of rolling bearing is weak and seriously affected by environmental noise, the fault feature information is difficult to identify. An early fault diagnosis method based on Ensemble local mean decompostion (ELMD) and maximum correlated kurtosis deconvolution (MCKD) is proposed. Firstly, the collected vibration signals are decomposed by ELMD. A finite number of product functions are obtained. Due to the noise interference, it is difficult to make a correct judgment on the fault from the spectrum of PF components. Then, the maximum correlation kurtosis deconvolution of PF components with fault features is carried out to eliminate the noise effect. Finally, the fault characteristic frequency of bearing can be accurately identified by the Hilbert envelope spectrum analysis of the noise reduction signal. The effectiveness and superiority of the method are verified by the bearing fault simulation experiment and engineering application.
【作者單位】： 內(nèi)蒙古科技大學(xué)機(jī)械工程學(xué)院;山東交通職業(yè)學(xué)院泰山校區(qū)機(jī)電系;
【基金】：國家自然科學(xué)基金項(xiàng)目(21366017) 內(nèi)蒙古高等學(xué)�？茖W(xué)研究項(xiàng)目(NYZY16154) 內(nèi)蒙古科技大學(xué)創(chuàng)新基金項(xiàng)目(2015QDL11)資助
【分類號】：TH133.33

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