本文關(guān)鍵詞：應(yīng)用AR模型的多參數(shù)與多測點信息融合的故障分類　出處：《機械科學(xué)與技術(shù)》2017年06期 　論文類型：期刊論文

  更多相關(guān)文章： 行星齒輪傳動系統(tǒng) 混沌特征參數(shù) 多測點 支持向量機 EMD AR模型

【摘要】：為了找到針對齒輪傳動系統(tǒng)多類故障分類的有效方法,對行星齒輪傳動系統(tǒng)進行故障實驗,獲取振動信號。采用EMD方法對該振動信號進行預(yù)處理,得到若干個IMF分量之和,對前4個有效的IMF分量分別建立AR模型,得到對應(yīng)的自回歸參數(shù)序列ф,進而對其分別計算關(guān)聯(lián)維數(shù)、最大Lyapunov指數(shù)、樣本熵這3個混沌特征參數(shù),并將其作為辨識特征量。將不同測點對應(yīng)的ф的不同混沌特征參數(shù)信息融合作為支持向量機的輸入向量,建立6種不同故障狀態(tài)的訓(xùn)練集,實現(xiàn)對故障類型進行分類。結(jié)果表明:對實驗獲取的振動信號進行EMD和AR模型處理后,能在很大程度上提高故障分類準確率。
[Abstract]:In order to find an effective method for the classification of many kinds of faults in gear transmission system, the fault experiment of planetary gear transmission system is carried out to obtain the vibration signal, and the vibration signal is preprocessed by EMD method. The sum of several IMF components is obtained. The AR model is established for the first four effective IMF components, and the corresponding autoregressive parameter sequences are obtained, and the correlation dimensions are calculated respectively. The maximum Lyapunov exponent and sample entropy are three chaotic characteristic parameters. The information of different chaotic characteristic parameters corresponding to different measuring points is fused as input vector of support vector machine, and six training sets of different fault states are established. The results show that the accuracy of fault classification can be improved to a great extent after the vibration signals obtained by experiments are processed by EMD and AR models.
【作者單位】： 天津工業(yè)大學(xué)機械工程學(xué)院;現(xiàn)代機電裝備技術(shù)重點實驗室;
【基金】：國家重大科技成果轉(zhuǎn)化項目(2060403) 天津市自然科學(xué)基金重點項目(10JCZDJC23400)資助
【分類號】：TH132.41
【正文快照】： 在風(fēng)力機的傳動系統(tǒng)中行星齒輪組的作用極其重要。然而,在運行過程中行星齒輪中的各部件極易出現(xiàn)損壞[1],導(dǎo)致系統(tǒng)出現(xiàn)各種故障。行星齒輪的故障特征極其微弱,難以獲取,但連帶的故障特征非常明顯。很難從時域和頻域中獲取有用的故障信息。因此,國內(nèi)外學(xué)者針對以上難點開始尋找，

本文編號：1369031