本文關(guān)鍵詞：基于Alpha穩(wěn)定分布特征參數(shù)的滾動軸承故障診斷研究　出處：《哈爾濱工業(yè)大學(xué)》2011年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 滾動軸承 Alpha穩(wěn)定分布 性能評估 故障診斷

【摘要】：滾動軸承作為機(jī)械設(shè)備中最常用的部件之一,其運行狀態(tài)是機(jī)械設(shè)備能否正常工作的重要影響因素。因此,對于滾動軸承的性能監(jiān)測以及故障診斷,其重要性不言而喻。當(dāng)滾動軸承在出現(xiàn)點蝕等故障時,很明顯的特點就是會在振動采樣信號中出現(xiàn)故障脈沖成份。在軸承故障初期,故障脈沖幅值較小,一般都會淹沒在采樣信號的噪聲之中,但到故障中后期時,故障脈沖幅值會越來越大,采樣信號本身也會呈現(xiàn)出明顯的脈沖特性。 本文首先從統(tǒng)計信號處理的角度出發(fā),通過分析滾動軸承故障仿真信號及其實際故障信號的非高斯性和脈沖特性,引入Alpha穩(wěn)定分布這一可用來描述具有明顯脈沖特性信號的統(tǒng)計模型。作為廣義化的高斯分布,Alpha穩(wěn)定分布擬合滾動軸承故障信號概率密度分布的精度更高,也更加的合理。 通過滾動軸承故障仿真信號的數(shù)學(xué)表達(dá)式,本文分析故障信號中軸承故障程度等因素對Alpha穩(wěn)定分布特征指數(shù)α及峭度值的影響,并明確了α值及峭度值只是反映信號本身的脈沖特性,與信號中的實際故障脈沖大小,即軸承故障程度并無直接關(guān)系這一結(jié)論。然后,通過進(jìn)一步分析對比α值及峭度值在滾動軸承性能評估的特點,提出一個新的基于Alpha穩(wěn)定分布概率密度分布的性能評估參數(shù)Lambda,通過仿真和實驗分析,指出其相對于α值及峭度值,具有對早期故障脈沖敏感度更高,晚期故障不會出現(xiàn)性能退化,且自身的敏感度可以根據(jù)實際需求調(diào)節(jié)的優(yōu)勢。 本文基于Alpha穩(wěn)定分布的特征參數(shù),提出了結(jié)合自適應(yīng)小波的滾動軸承早期故障信號檢測方法,以及結(jié)合神經(jīng)網(wǎng)絡(luò)的滾動軸承故障分類方法,通過實驗數(shù)據(jù)分析,表明將現(xiàn)代信號處理方法同Alpha穩(wěn)定分布結(jié)合的故障信號診斷方法,可以有效地分析滾動軸承故障信號,并且表現(xiàn)出了一定的優(yōu)勢。
[Abstract]:Rolling bearing is one of the most common parts of the mechanical equipment, its running status is an important factor influencing the mechanical equipment can work normally. Therefore, the rolling bearing performance monitoring and fault diagnosis, and its importance is self-evident. When there is pitting in rolling bearings fault, it's very clear that in the sampling pulse vibration fault ingredients appear in rolling bearing fault signal. The early fault pulse amplitude is small, usually submerged in the noise signal, but in the late fault, the fault pulse amplitude will become increasingly large, the sampling signal itself will clearly show the pulse characteristics.
Firstly, from the angle of statistical signal processing, non Gauss and pulse characteristics simulation fault signal of rolling bearings and the actual fault signal by analyzing the Alpha stable distribution which can be used to describe a statistical model was introduced. The characteristics of pulse signal distribution as a generalized Gauss Alpha stable distribution of the probability density distribution of rolling bearing fault the signal of higher accuracy and more reasonable.
The mathematical expression of fault simulation signal of the rolling bearing, the factors of bearing fault degree of fault signal analysis of the impact on the Alpha stable distribution characteristic exponent and kurtosis, and the alpha value and kurtosis value only reflect the pulse characteristics of the signal itself, and the actual size of fault pulse signal, namely bearing fault degree there is no direct relationship between this conclusion. Then, through further comparative analysis of alpha value and kurtosis in the characteristics of the performance evaluation of rolling bearings, put forward a Lambda performance evaluation of Alpha stable distribution of probability density distribution based on the new, through simulation and experimental analysis, pointed out that compared with the alpha value and kurtosis value is on early fault pulse sensitive late fault no performance degradation, and the sensitivity can be adjusted according to the actual needs of the advantage.
The characteristic parameters based on Alpha stable distribution, is presented based on adaptive wavelet of rolling bearing early fault signal detection method and fault classification method combined with neural network, by analyzing the experimental data show that the modern signal processing method with fault signal diagnosis method combining with Alpha stable distribution, can effectively analyze the fault signals of rolling bearing, and showed certain advantages.

【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH165.3

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本文編號：1359409