【摘要】：滾動(dòng)軸承是工業(yè)領(lǐng)域關(guān)鍵元件之一,剝落是滾動(dòng)軸承重要的失效形式,通常經(jīng)滾動(dòng)體與滾道表面的交變應(yīng)力作用而在表面以下薄弱點(diǎn)處形成裂紋核心,隨后裂紋擴(kuò)展至材料表面而形成點(diǎn)蝕和剝落。及時(shí)獲取滾動(dòng)軸承性能狀態(tài)直接關(guān)系到企業(yè)能否合理制定設(shè)備維護(hù)、維修、備品、備件計(jì)劃,以應(yīng)付突發(fā)狀況,避免經(jīng)濟(jì)損失、重大事故甚至人員傷亡等情況的發(fā)生,并可合理利用滾動(dòng)軸承的使用壽命,充分挖掘其工作潛力而避免浪費(fèi)。因此,掌握滾動(dòng)軸承的疲勞演化進(jìn)程將有助于提高企業(yè)經(jīng)濟(jì)效益,也有助于對(duì)滾動(dòng)軸承損傷機(jī)理的科學(xué)研究。此外,準(zhǔn)確的疲勞演化數(shù)據(jù)是滾動(dòng)軸承性能退化評(píng)估、壽命預(yù)測(cè)等理論的重要數(shù)據(jù)來(lái)源,同時(shí)也是其計(jì)算結(jié)果的精度保障。但滾動(dòng)軸承的壽命離散度大,采用基于概率理論、斷裂力學(xué)與損傷建模等手段均不能較好的獲取滾動(dòng)軸承個(gè)體疲勞信息。而經(jīng)典的基于振動(dòng)等狀態(tài)監(jiān)測(cè)與故障診斷技術(shù)僅能獲取表面已損傷的狀態(tài),對(duì)早期疲勞探測(cè)有一定局限性。材料受力變形或裂紋擴(kuò)展將產(chǎn)生聲發(fā)射信號(hào)(Acoustic Emission,AE)。二十世紀(jì)五十年代初期,由德國(guó)學(xué)者Kaiser開拓性的研究工作推動(dòng)了現(xiàn)代聲發(fā)射技術(shù)的誕生與發(fā)展,并逐漸成為獲取滾動(dòng)軸承疲勞早期損傷信息的有力工具。聲發(fā)射技術(shù)可高效、可靠的檢測(cè)材料表面下的活性缺陷。自然失效的軸承AE數(shù)據(jù)雖然可較為準(zhǔn)確的反應(yīng)其疲勞演化進(jìn)程,但時(shí)間成本高,而現(xiàn)有的疲勞試驗(yàn)方案?jìng)鬟f路徑復(fù)雜,信號(hào)衰減大。且AE信號(hào)中存在難以避免的噪聲,常規(guī)手段難以應(yīng)對(duì),尤其損傷初期,信號(hào)微弱而易被湮沒。現(xiàn)有的獲取滾動(dòng)軸承疲勞進(jìn)程的方法多采用3~5個(gè)傳統(tǒng)AE指標(biāo)的趨勢(shì)分析,存在采用人工確定固定門檻電壓而易引入主觀干擾等諸多不足,且采用少數(shù)特征不足以全面反映滾動(dòng)軸承疲勞狀態(tài),特征與損傷關(guān)系的研究還不夠深入。而采用多數(shù)特征又會(huì)增加計(jì)算負(fù)擔(dān),特征間的冗余與不相關(guān)會(huì)干擾對(duì)疲勞進(jìn)程信息的獲取。加之各特征敏感性不同,演化信息不均勻地分散于各特征中,需要一定的專業(yè)知識(shí)與從業(yè)經(jīng)驗(yàn)。逐一分析不僅耗時(shí),而且工作量大,這將對(duì)演化進(jìn)程的判定造成不便。因此,準(zhǔn)確有效地提取滾動(dòng)軸承疲勞演化特征的方法將成為解決上述問(wèn)題的關(guān)鍵。本學(xué)位論文在國(guó)家自然科學(xué)基金項(xiàng)目"基于聲發(fā)射和數(shù)值模型的球軸承點(diǎn)蝕疲勞壽命估計(jì)研究"(項(xiàng)目編號(hào):51465022),國(guó)家自然科學(xué)基金"水下運(yùn)動(dòng)目標(biāo)時(shí)變?cè)肼晥?chǎng)欠定盲提取模型及其算法研究"(項(xiàng)目編號(hào):51265018)等的資助下,以聲發(fā)射技術(shù)為基礎(chǔ),圍繞滾動(dòng)軸承自無(wú)損傷至剝落失效的全壽命階段中疲勞演化特征信息的有效提取問(wèn)題,采用理論研究、試驗(yàn)驗(yàn)證相結(jié)合的研究路線,初步建立了基于聲發(fā)射的滾動(dòng)軸承接觸疲勞演化信息的提取方法框架,主要包括如下內(nèi)容:(1)結(jié)合滾動(dòng)軸承狀態(tài)監(jiān)測(cè)理論和工程需求,回顧了滾動(dòng)軸承疲勞的相關(guān)研究方法、聲發(fā)射理論和聲發(fā)射監(jiān)測(cè)技術(shù)、降噪技術(shù)、特征評(píng)估及特征提取技術(shù)的國(guó)內(nèi)外研究現(xiàn)狀,并對(duì)其優(yōu)缺點(diǎn)進(jìn)行了評(píng)述,確定了本文研究?jī)?nèi)容與技術(shù)路線。(2)分析了傳統(tǒng)聲發(fā)射監(jiān)測(cè)指標(biāo)存在的不足,給出了一種基于浮動(dòng)閾值與平均化的AE監(jiān)測(cè)指標(biāo),開展了多指標(biāo)趨勢(shì)分析。分析表明,改進(jìn)的AE指標(biāo)能更好的給出疲勞演化信息,隨后篩選出了多個(gè)對(duì)球軸承疲勞較為敏感的指標(biāo),并對(duì)球軸承的一般演化規(guī)律進(jìn)行了歸納與總結(jié)。設(shè)計(jì)了一種新型滾動(dòng)軸承疲勞試驗(yàn)臺(tái),為后續(xù)的理論研究提供了試驗(yàn)驗(yàn)證平臺(tái)。(3)針對(duì)采集的聲發(fā)射信號(hào)中易混入噪聲的問(wèn)題,深入研究了聲發(fā)射信號(hào)中的噪聲來(lái)源與特性并給出了常規(guī)處理辦法。對(duì)常規(guī)處理難以解決的噪聲,從聲發(fā)射信號(hào)中噪聲與引入的噪聲在自相關(guān)形式上的相似性角度,提出了一種基于二次相關(guān)理論的加權(quán)閾值小波包降噪算法,并進(jìn)行了仿真和實(shí)測(cè)推力球軸承聲發(fā)射信號(hào)的試驗(yàn)研究。研究表明,該方法能夠較好的抑制采集聲發(fā)射信號(hào)的噪聲,信噪比提升穩(wěn)定,降噪后的聲發(fā)射信號(hào)沖擊明顯。(4)分析了現(xiàn)有距離評(píng)估方法的不足。針對(duì)多參數(shù)中可能存在的不相關(guān)與冗余性而干擾滾動(dòng)軸承損傷程度信息準(zhǔn)確識(shí)別的問(wèn)題,以傳統(tǒng)距離評(píng)估方法為理論基礎(chǔ),從算法對(duì)提升目標(biāo)識(shí)別的準(zhǔn)確性與穩(wěn)定性角度,提出了一種基于位置補(bǔ)償系數(shù)的距離評(píng)估方法。試驗(yàn)研究表明,通過(guò)該算法篩選出的敏感特征集具有較高的損傷識(shí)別精度,相對(duì)其他算法,該算法的穩(wěn)定性更好,分類正確率更高。(5)在綜合上述算法的基礎(chǔ)上,以最少特征成分涵蓋最多有效信息為目標(biāo),提出了一種基于改進(jìn)粒子群優(yōu)化核熵成分分析的疲勞演化信息提取算法。隨后,根據(jù)熵的計(jì)算法則,又提出了一種二次特征融合算法,將已融合特征進(jìn)一步融合,以更為高效的提取滾動(dòng)軸承演化信息。經(jīng)實(shí)測(cè)AE數(shù)據(jù)分析表明,改進(jìn)的核熵成分分析的主要核熵得分?jǐn)?shù)據(jù)能有效識(shí)別滾動(dòng)軸承疲勞演化階段,二次融合特征提取算法可極大匯聚來(lái)自各維特征的疲勞演化信息,僅采用單一的二次融合特征即可便捷、有效地表征滾動(dòng)軸承的疲勞進(jìn)程。
[Abstract]:Rolling bearing is one of the key components in the industrial field. Peeling is an important failure form of rolling bearing. It usually forms a crack core at the weak point below the surface by alternating stress between the roller and the raceway surface, and then the crack expands to the material surface to form pitting corrosion and peeling. Whether the enterprise can reasonably formulate equipment maintenance, maintenance, spare parts, spare parts plan to cope with unexpected situations, avoid economic losses, major accidents and even casualties, etc., and can reasonably use the service life of rolling bearings, fully tap their working potential and avoid waste. Therefore, mastering the fatigue evolution process of rolling bearings will have In addition, the accurate fatigue evolution data is an important data source for the theory of performance degradation assessment and life prediction of rolling bearings, and it is also an accurate guarantee for the calculation results. The classical state monitoring and fault diagnosis technology based on vibration can only obtain the damaged state of the surface, which has certain limitations for early fatigue detection. In the early 1950s, the pioneering work of Kaiser, a German scholar, promoted the birth and development of modern acoustic emission technology, and gradually became a powerful tool to obtain early fatigue damage information of rolling bearings. Although AE data of bearing can accurately reflect its fatigue evolution process, the time cost is high, and the existing fatigue test schemes have complex transmission paths and large signal attenuation. The process method usually adopts trend analysis of 3-5 traditional AE indexes, which has many shortcomings, such as manual determination of fixed threshold voltage and easy introduction of subjective disturbance. A few features are not enough to fully reflect the fatigue state of rolling bearings, and the research on the relationship between characteristics and damage is not deep enough. The redundancy and irrelevance between features will interfere with the acquisition of fatigue process information. Moreover, different characteristics have different sensitivities. Evolutionary information is unevenly distributed among the features, requiring a certain amount of professional knowledge and experience. Effective extraction of fatigue evolution characteristics of rolling bearings is the key to solve the above problems. This dissertation is based on the National Natural Science Foundation project "Research on pitting fatigue life estimation of ball bearings based on acoustic emission and numerical model" (item number: 51465022), and the National Natural Science Foundation of China (NSFC), "Undetermined time-varying noise field of underwater moving targets" Blind Extraction Model and Its Algorithms Research (Project Number: 51265018) and other funding, based on acoustic emission technology, around the problem of effective extraction of fatigue evolution characteristic information in the whole life stage of rolling bearings from non-destructive to peeling failure, a research route combining theoretical research with experimental verification was established preliminarily based on acoustic emission technology. The extraction method framework of rolling bearing contact fatigue evolution information mainly includes the following contents: (1) Combining with rolling bearing condition monitoring theory and engineering requirements, this paper reviews the related research methods of rolling bearing fatigue, acoustic emission theory and acoustic emission monitoring technology, noise reduction technology, feature evaluation and feature extraction technology at home and abroad. (2) The shortcomings of traditional AE monitoring indexes are analyzed, and an AE monitoring index based on floating threshold and averaging is given. The trend analysis of multiple indexes is carried out. The analysis shows that the improved AE index can give better fatigue evolution information. A new type of rolling bearing fatigue test bench is designed, which provides a test and verification platform for subsequent theoretical research. (3) Aiming at the problem of easy mixing noise in the acoustic emission signals collected, in-depth study is carried out. The noise sources and characteristics of AE signals are analyzed and the conventional processing methods are given. For the noise which is difficult to be solved by conventional processing, a weighted threshold wavelet packet denoising algorithm based on quadratic correlation theory is proposed from the angle of the similarity of the autocorrelation form between the noise in AE signals and the introduced noise, and the simulation and implementation are carried out. Experimental study on acoustic emission signals of thrust ball bearings shows that the method can suppress the noise of acoustic emission signals, improve the signal-to-noise ratio and stabilize the acoustic emission signals. The impact of acoustic emission signals after noise reduction is obvious. (4) The shortcomings of existing distance evaluation methods are analyzed. On the basis of traditional distance evaluation method, a distance evaluation method based on position compensation coefficient is proposed to improve the accuracy and stability of target recognition. Experimental results show that the sensitive feature set selected by this method has higher damage identification. Fifthly, based on the above algorithm, a fatigue evolution information extraction algorithm based on Improved Particle Swarm Optimization (PSO) kernel entropy component analysis is proposed, which covers the least feature components and the most effective information. A quadratic feature fusion algorithm is proposed to fuse the fused features to extract the evolution information of rolling bearings more efficiently. The results of AE data analysis show that the improved kernel entropy analysis can effectively identify the fatigue evolution stage of rolling bearings and the quadratic fusion feature extraction algorithm can converge greatly. The fatigue evolution information from each dimension feature is gathered, and the fatigue process of rolling bearings can be easily and effectively characterized by a single secondary fusion feature.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH133.33

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