本文關(guān)鍵詞： 局部特征尺度分解 基本尺度熵 相空間重構(gòu) 多尺度 支持向量機(jī)　出處：《機(jī)械傳動(dòng)》2017年09期 　論文類型：期刊論文

【摘要】：為了準(zhǔn)確進(jìn)行軸承故障診斷,提出了基于局部特征尺度分解與基本尺度熵的故障特征提取及診斷方法。首先,分析了基本尺度熵提取軸承振動(dòng)信號(hào)蘊(yùn)涵的故障信息的合理性,針對(duì)基本尺度熵的參數(shù)選擇問題,提出了基于相空間重構(gòu)理論的延遲時(shí)間和嵌入維數(shù)選擇方法;然后,運(yùn)用局部特征尺度分解對(duì)基本尺度熵進(jìn)行自適應(yīng)多尺度化,充分提取了故障特征;最后,將原始信號(hào)的降噪數(shù)據(jù)及有用分量的基本尺度熵作為特征向量,通過支持向量機(jī)進(jìn)行故障診斷。以軸承振動(dòng)試驗(yàn)信號(hào)為例進(jìn)行了驗(yàn)證,結(jié)果表明,所提方法能有效識(shí)別正常、內(nèi)圈故障、外圈故障及滾動(dòng)體故障等4種狀態(tài)。
[Abstract]:In order to accurately diagnose the bearing, put forward the fault feature extraction and diagnosis methods and basic scale entropy scale decomposition based on local features. Firstly, analyze the rationality of the fault information basic scale entropy to extract the implication of bearing vibration signal, aiming at the problem of parameter selection of basic scale entropy, put forward the theory of phase space reconstruction delay time and then based on the method of embedding dimension; the basic scale entropy, multi-scale adaptive decomposition using local characteristic scale, to fully extract the fault features; finally, the basic scale entropy reduction data of the original signal and the useful component as the feature vector of fault diagnosis based on support vector machine. In order to test the bearing vibration signal is performed the results show that the verification, the proposed method can effectively identify the normal, inner ring fault, outer ring fault and rolling fault 4.

【作者單位】： 海軍航空工程學(xué)院;
【基金】：國家部委預(yù)研基金(9140A27020214JB1446)
【分類號(hào)】：TH133.3
【正文快照】： 0引言旋轉(zhuǎn)機(jī)械發(fā)生故障時(shí),其沖擊成分被系統(tǒng)固有振動(dòng)所調(diào)制,引起了動(dòng)力學(xué)突變,其振動(dòng)信號(hào)的時(shí)域波形會(huì)產(chǎn)生新的振動(dòng)模式,體現(xiàn)為復(fù)雜程度的變化。因此,運(yùn)用復(fù)雜性指標(biāo)可提取旋轉(zhuǎn)機(jī)械的運(yùn)行狀態(tài)。在復(fù)雜性指標(biāo)中,復(fù)雜度反映了時(shí)間序列隨著長度的增加而出現(xiàn)新模式的速率,能夠描述，

本文編號(hào)：1466042