本文關鍵詞： 局部均值分解 基本尺度熵 滾動軸承 故障診斷 AP聚類算法　出處：《計算機應用研究》2017年06期 　論文類型：期刊論文

【摘要】：針對滾動軸承聚類故障聚類模式識別方法中需要預先設定聚類數(shù)目問題,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)滾動軸承聚類故障診斷方法。該方法首先使用LMD模型將滾動軸承的不同狀態(tài)振動信號分解為若干乘積函數(shù)(production function,PF);其次使用BSE計算前三個PF的熵值(BSE1-BSE3),并將其作為AP的輸入進行滾動軸承的故障模式識別。最后實驗結果表明,在不需要劃分聚類中心個數(shù)的前提條件下AP聚類模型對滾動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】： 武漢大學自動化系;
【基金】：中央高�；究蒲袑ｍ椯Y金資助項目(121031)
【分類號】：TH133.33;TP311.13
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本文編號：1529791