發(fā)布時間：2018-03-21 16:47

  本文選題：軸承故障診斷　切入點(diǎn)：數(shù)學(xué)形態(tài)學(xué)　出處：《武漢科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：軸承是機(jī)械設(shè)備中的重要零部件，在機(jī)械系統(tǒng)中應(yīng)用廣泛，但軸承失效時往往會影響機(jī)械運(yùn)作，嚴(yán)重時可能會引發(fā)機(jī)械系統(tǒng)癱瘓，甚至發(fā)生傷亡事故給企業(yè)帶來巨大經(jīng)濟(jì)損失。根據(jù)統(tǒng)計(jì)數(shù)據(jù)顯示，因軸承失效而引發(fā)的機(jī)械故障在總體機(jī)械故障中占有較大比重。因此，對滾動軸承進(jìn)行早期故障監(jiān)測，找出故障發(fā)生的位置，預(yù)測故障發(fā)展方向，具有十分重要的意義。 軸承故障診斷中的重點(diǎn)：從待處理信號中提取軸承故障特征信息，判斷軸承故障類型和故障部位。本文在學(xué)習(xí)了軸承故障的類型和特征的基礎(chǔ)上，針對傳統(tǒng)形態(tài)學(xué)在滾動軸承故障信號處理中，結(jié)構(gòu)元素選取困難，降噪效果不理想等問題，提出了廣義差值濾波算法和自適應(yīng)張量形態(tài)學(xué)算法。本文的主要研究內(nèi)容： 1、提出了廣義形態(tài)差值濾波的軸承故障特征提取算法。根據(jù)待處理信號的局部特征信息，選取最優(yōu)結(jié)構(gòu)元素尺度，構(gòu)建廣義差值形態(tài)濾波器，可以更好的提取故障特征信息。通過仿真信號和軸承模擬故障實(shí)驗(yàn)信號證明了該方法是有效的。 2、在廣義形態(tài)差值濾波算法的基礎(chǔ)上，為了進(jìn)一步提高形態(tài)學(xué)的降噪和特征提取效果，提出了一種自適應(yīng)張量形態(tài)學(xué)的軸承故障特征提取算法。該算法根據(jù)待處理信號的局部特征信息，構(gòu)建張量橢圓結(jié)構(gòu)元素，能取代傳統(tǒng)的直線型結(jié)構(gòu)元素和圓盤結(jié)構(gòu)元素。利用張量形態(tài)學(xué)濾波器對軸承故障信號進(jìn)行降噪和提取故障特征，在軸承故障中得到了很好的應(yīng)用效果。 3、對比三種形態(tài)處理方法在軸承故障特征提取中的優(yōu)劣，綜合分析結(jié)果表明：自適應(yīng)張量形態(tài)學(xué)在軸承內(nèi)、外圈故障的特征提取中效果最優(yōu)，其次是廣義差值形態(tài)學(xué)提取效果，最末的是傳統(tǒng)形態(tài)學(xué)提取效果；而針對軸承滾動體故障信號分析，傳統(tǒng)形態(tài)學(xué)提取效果最優(yōu)，，其次是自適應(yīng)張量形態(tài)學(xué)和廣義差值形態(tài)學(xué)。
[Abstract]:Bearing is an important part in mechanical equipment, which is widely used in mechanical system. However, bearing failure often affects mechanical operation and may lead to mechanical system paralysis when it is serious. According to the statistical data, the mechanical failure caused by bearing failure occupies a large proportion of the total mechanical failure. Therefore, the early fault monitoring of rolling bearing is carried out. It is of great significance to find out the location of the fault and predict the development direction of the fault. The key points in bearing fault diagnosis are to extract the bearing fault feature information from the signal to be processed, to judge the bearing fault type and fault location. Aiming at the problems of traditional morphology in fault signal processing of rolling bearing, such as difficult selection of structural elements and unsatisfactory noise reduction effect, the generalized difference filtering algorithm and adaptive Zhang Liang morphological algorithm are proposed. The main research contents of this paper are as follows:. 1. A bearing fault feature extraction algorithm based on generalized morphological difference filtering is proposed. According to the local feature information of the signal to be processed, the optimal structural element scale is selected to construct the generalized difference morphological filter. The fault characteristic information can be extracted better, and the method is proved to be effective by simulation signal and bearing simulation test signal. 2. On the basis of the generalized morphological difference filtering algorithm, in order to further improve the morphological noise reduction and feature extraction, An adaptive Zhang Liang morphology based bearing fault feature extraction algorithm is proposed in this paper. According to the local feature information of the signal to be processed, the elliptical structure element of Zhang Liang is constructed. Zhang Liang morphological filter is used to reduce noise and extract fault feature of bearing fault signal, which can replace the traditional linear structure element and disk structure element, and has a good application effect in bearing fault. 3. Comparing the advantages and disadvantages of the three kinds of morphological processing methods in the bearing fault feature extraction, the comprehensive analysis results show that the adaptive Zhang Liang morphology is the best in the feature extraction of the inner and outer ring faults of the bearing, the second is the generalized difference morphological extraction effect. The last one is the traditional morphology extraction effect, and the traditional morphology extraction effect is the best for the fault signal analysis of bearing rolling body, followed by the adaptive Zhang Liang morphology and the generalized difference morphology.
【學(xué)位授予單位】：武漢科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TH133.3;TH165.3

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