發(fā)布時間：2018-03-17 07:31

  本文選題：軸承故障診斷　切入點：經(jīng)驗小波變換　出處：《電子測量與儀器學報》2017年08期 　論文類型：期刊論文

【摘要】：實際應用中研究機械系統(tǒng)的工作狀態(tài)時,通常會對其所產(chǎn)的信號進行研究分析,從而得出相關結論。這些由機械系統(tǒng)產(chǎn)生的信號一般含有多種不同波動的混合成分,為了得出可靠的結論,必須從復合信號和背景噪聲中分離出有物理意義的成分。因此引入一種新的故障提取方法,首先利用一種較新的模態(tài)分解算法——經(jīng)驗小波變換,將一組信號分解成多個具有緊支撐傅里葉頻譜的調(diào)幅-調(diào)頻(AM-FM)分量;然后利用K-L散度值挑選出具有物理意義的分量;最后將挑選出的分量通過三點對稱差分能量算子運算,得到其能量譜的同時也能得到瞬時頻率,從而提取出故障特征。將該方法用于模擬信號和實際軸承故障信號,并且同之前的方法進行對比。結論表明,該方法不僅能很好的提取軸承故障特征,而且證明該方法具有更好的優(yōu)越性。
[Abstract]:When studying the working state of a mechanical system in practical application, the signals produced by the mechanical system are usually studied and analyzed, and the relevant conclusions are drawn. These signals produced by the mechanical system generally contain a variety of mixed components with different fluctuations. In order to get a reliable conclusion, the physical components must be separated from the composite signal and background noise. Therefore, a new fault extraction method is introduced. Firstly, a new mode decomposition algorithm, empirical wavelet transform, is used. A set of signals is decomposed into several amplitude-frequency modulation (AM FM) components with compact support Fourier spectrum, and then the physical components are selected by using K-L divergence value. Finally, the selected components are calculated by a three point symmetric differential energy operator. At the same time, the instantaneous frequency can be obtained, and the fault characteristics can be extracted. The method is used to simulate the signal and the actual bearing fault signal, and compared with the previous method. The conclusion shows that, This method can not only extract the bearing fault features well, but also prove that this method has better advantages.
【作者單位】： 長安大學道路施工技術與裝備教育部重點實驗室;
【基金】：中央高校教育教學改革專項經(jīng)費建設項目(jgy16049,0012-310600161000)資助
【分類號】：TH133.3

【相似文獻】

相關期刊論文 前10條

1 胡靜濤;郭前進;;基于線調(diào)頻小波變換的旋轉(zhuǎn)機械故障診斷[J];儀器儀表學報;2006年S1期

2 段晨東;李凌均;何正嘉;;基于提升模式的非抽樣小波變換及其在故障診斷中的應用[J];機械強度;2006年06期

3 彭曉紅;彭惠芬;;基于小波變換的軸力桿裂紋識別[J];中國設備工程;2009年06期

4 程珩,杜嵐松;小波變換在故障診斷中的應用[J];山西機械;2003年04期

5 段晨東,李凌均,何正嘉;第二代小波變換在旋轉(zhuǎn)機械故障診斷中的應用[J];機械科學與技術;2004年02期

6 柳長昕;王鋒;劉傳海;柳穎;柳振河;吳世光;黎世翔;;基于小波變換的隱Markov模型離心泵故障診斷方法研究[J];現(xiàn)代機械;2009年01期

7 丁凱;柳兵;付士鵬;張金會;朱霄s，

本文編號：1623772