本文關(guān)鍵詞： 局部均值分解 基本尺度熵 滾動(dòng)軸承 故障診斷 AP聚類算法　出處：《計(jì)算機(jī)應(yīng)用研究》2017年06期 　論文類型：期刊論文

【摘要】：針對(duì)滾動(dòng)軸承聚類故障聚類模式識(shí)別方法中需要預(yù)先設(shè)定聚類數(shù)目問(wèn)題,提出了一種基于局部均值分解(local mean decompoeiton,LMD)與基本尺度熵(base scale entropy,BSE)的相鄰傳播(affinity propagation,AP)滾動(dòng)軸承聚類故障診斷方法。該方法首先使用LMD模型將滾動(dòng)軸承的不同狀態(tài)振動(dòng)信號(hào)分解為若干乘積函數(shù)(production function,PF);其次使用BSE計(jì)算前三個(gè)PF的熵值(BSE1-BSE3),并將其作為AP的輸入進(jìn)行滾動(dòng)軸承的故障模式識(shí)別。最后實(shí)驗(yàn)結(jié)果表明,在不需要?jiǎng)澐志垲愔行膫�(gè)數(shù)的前提條件下AP聚類模型對(duì)滾動(dòng)軸承的故障劃分效果較好。
[Abstract]:In order to solve the problem that the number of clusters should be set in advance in the method of rolling bearing clustering fault clustering pattern recognition, In this paper, a method of clustering fault diagnosis of rolling bearings based on local mean decomposition (LMD) and basic scale entropy (scale entropyp) is presented. The LMD model is used to divide the vibration signals of rolling bearings in different states. The results show that the entropy of the first three PF is calculated by using BSE, and it is used as the input of AP to recognize the fault pattern of the rolling bearing. Without the need to divide the number of cluster centers, AP clustering model has better effect on rolling bearing fault classification.
【作者單位】： 武漢大學(xué)自動(dòng)化系;
【基金】：中央高�；究蒲袑ｍ�(xiàng)資金資助項(xiàng)目(121031)
【分類號(hào)】：TH133.33;TP311.13
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本文編號(hào)：1529791