基于EMD分解的聚類樹狀圖軸承故障診斷
張梅軍,韓思晨,王闖,焦志鑫
(解放军理工大学 工程兵工程学院,江苏 南京 210007)
摘要:針對滾動軸承故障振動信號的非平穩特征和故障征兆模糊性,提出了基于EMD和動態模糊聚類圖的軸承故障診斷方法。運用EMD方法提取待診斷的軸承運行狀態樣本的能量特征指標,應用模糊聚類分析方法對特征參數進行聚類,並作出聚類樹狀圖。結果表明,該方法不需要大量的樣本進行學習,且能更直觀、准確識別滾動軸承的運行狀態。
關鍵詞:EMD分解;動態模糊聚類圖;故障診斷
中圖分類號:O242.21        文獻標識碼:A         文章編號:10060316 (2012) 07000104
Clustering based on EMD decomposition tree bearing fault diagnosis
ZHANG Mei-jun,HAN Si-chen,WANG Chuang,JIAO Zhi-xin
(Engineering Institute of Engineering Corps,PLA University of Science,Nanjing 210007,China)
Abstract:For the non-stationary feature of a vibration signal of defective rolling bearings and the ambiguity of fault feature, a fault diagnosis method of rolling bearings is proposed using EMD ( Empirical Mode Decomposition ), Dynamic fuzzy clustering graph. Firstly, an EMD method was used to decompose a vibration signal of a rolling bearing. Then those parameters were analyzed by fuzzy clustering algorithm, and plotted amic fuzzy clustering graph. Experiments indicated that This method does not require a large number of samples for learning, and And can more intuitivelt, accurately distinguish the running state of bearings.
Key wordsemp iricalmode decomposition ( EMD );dynamic fuzzy clustering graph;fault diagnosis

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收稿日期:2011-02-29
基金項目:國家自然科學基金資助項目(51175511)
作者簡介:張梅軍(1958-),女,江蘇宜興人,副教授,碩士生導師,主要研究方向爲故障診斷和工程機械動力學。

 

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