本文選題：滾動軸承　切入點(diǎn)：振動信號　出處：《哈爾濱工業(yè)大學(xué)》2015年博士論文　論文類型：學(xué)位論文

【摘要】：滾動軸承是眾多旋轉(zhuǎn)機(jī)械的關(guān)鍵性部件,被人們稱為機(jī)器的關(guān)節(jié)。其在極端環(huán)境下,受各種因素的影響,是整個旋轉(zhuǎn)機(jī)械系統(tǒng)中可靠性最差的零部件,成為“水桶短板”,直接影響整個機(jī)械設(shè)備的運(yùn)行可靠性。滾動軸承運(yùn)行時,其性能一般會從正常狀態(tài)逐漸衰退直至完全損壞。如果能在軸承損壞過程中檢測到它的性能退化程度或同時檢測到故障位置及性能退化程度,就能夠變傳統(tǒng)的定時或事后維修為視情維修,實現(xiàn)軸承的主動維護(hù)。這樣可最大限度利用軸承壽命,降低維護(hù)保障成本,避免進(jìn)一步導(dǎo)致事故發(fā)生,造成巨大的損失。該研究方向側(cè)重于設(shè)備整體性能的研究,是從理念和方法上對現(xiàn)有故障診斷技術(shù)的拓展與深入。本文以滾動軸承為研究對象,對滾動軸承非平穩(wěn)振動信號進(jìn)行分析與處理。以實現(xiàn)一種滾動軸承不同狀態(tài)(正常狀態(tài),內(nèi)環(huán)、滾動體、外環(huán)故障狀態(tài)及其不同性能退化程度)智能評估方法為目標(biāo),在深入研究振動信號特征提取與約簡方法的基礎(chǔ)上,進(jìn)一步深入研究超球結(jié)構(gòu)多類支持向量機(jī)的優(yōu)化問題,以及如何建立滾動軸承多狀態(tài)評估模型這一關(guān)鍵技術(shù)問題。論文的主要工作包括:(1)基于集合經(jīng)驗?zāi)B(tài)分解(EEMD)的振動信號時頻分析方法研究。對比分析驗證了基于EEMD的Hilbert譜時頻分析方法具有時間和頻率分辨率高、抗模態(tài)混疊的特點(diǎn)。并針對EEMD分解時,加入噪聲幅值大小和集合平均次數(shù)這2個重要參數(shù)的選取問題,從能量標(biāo)準(zhǔn)差的角度,研究EEMD方法中加入白噪聲的準(zhǔn)則;針對滾動軸承振動信號經(jīng)改進(jìn)的EEMD分解后得到的固有模態(tài)函數(shù)(IMF)仍含有虛假分量和對滾動軸承故障不敏感的IMF分量問題,研究一種峭度值結(jié)合歸一化相關(guān)系數(shù)的IMF提存算法。實驗研究驗證了所提方法的有效性,為后續(xù)進(jìn)一步提取特征打下堅實的基礎(chǔ)。(2)振動信號多域特征提取與約簡方法研究。為了精細(xì)刻畫滾動軸承運(yùn)行狀態(tài),體現(xiàn)滾動軸承振動信號的全局特征以及局部特征,研究時域、頻域和時頻域的多域特征提取方案。其中,時頻域特征提取方面提出了基于改進(jìn)EEMD敏感IMF分別結(jié)合時域指標(biāo)、頻域指標(biāo)、自回歸模型和奇異值分解的方法。基于此構(gòu)造了滾動軸承單個樣本的特征向量以及各狀態(tài)的特征向量矩陣,并建立了滾動軸承各狀態(tài)特征庫。針對高維特征之間存在相關(guān)性和冗余性的問題,研究流形學(xué)習(xí)算法,結(jié)合支持向量機(jī)(SVM)通過實驗對比分析,確定了對滾動軸承特征約簡最有效的方法。(3)智能分類方法及故障智能診斷方法研究。超球結(jié)構(gòu)多類SVM雖具有一系列優(yōu)點(diǎn),但其分類精度與普通SVM相比并不高。針對此問題,研究分類規(guī)則,提出改進(jìn)方案,并對關(guān)鍵區(qū)域提出了新的決策準(zhǔn)則。同時,針對經(jīng)驗確定超球結(jié)構(gòu)多類SVM核參數(shù)選取范圍的問題,推導(dǎo)超球球心間的距離計算公式,提出將球心間的距離作為分離指數(shù)確定核參數(shù)的最優(yōu)選取范圍,達(dá)到了降低訓(xùn)練時間消耗的目的。深入研究了滾動軸承不同運(yùn)行狀態(tài)的智能故障診斷方法。建立了超球結(jié)構(gòu)多類SVM智能診斷模型,并進(jìn)行了大量實驗,驗證了所提方法的有效性。(4)滾動軸承狀態(tài)評估方法研究。針對滾動軸承故障智能診斷方法只能判斷軸承故障狀態(tài)的從屬關(guān)系,不能對損傷程度和故障變化進(jìn)行量化描述,以此來定量評估其性能狀態(tài)的問題,從SVM分類原理、滾動軸承結(jié)構(gòu)及傳感器安裝位置的振動傳播機(jī)理角度分析,提出基于SVM的補(bǔ)償相對距離的評估指標(biāo);從改進(jìn)超球結(jié)構(gòu)多類SVM原理、特征向量的方向、各狀態(tài)超球的位置關(guān)系多方面分析,又提出夾角余弦距離補(bǔ)償廣義最小距離的評估指標(biāo),建立了智能評估模型。通過滾動軸承不完備振動數(shù)據(jù)和全壽命完備數(shù)據(jù)兩方面的實驗研究,對比分析了各評估模型的性能。
[Abstract]:The rolling bearing is the key component of large rotating machinery, known as the machine joints. In extreme environments, affected by various factors, is the worst of the rotating parts reliability of mechanical systems, a "bucket short board", directly affects the reliability of the whole machine. The rolling operation, the performance will gradually decline from the normal state until completely damaged. If to its performance in detection of bearing damage in the process of degradation degree or detected fault position and performance degradation degree, can change the traditional timing or maintenance for maintenance, maintenance to achieve active bearing. It can maximize the use of bearing life, reduce maintenance costs, avoid further lead to accidents, resulting in huge losses. The research direction focuses on the overall performance of the equipment from the idea and the method Further exploration on the existing fault diagnosis technology. In this paper, the rolling bearing as the research object, the rolling bearing of non-stationary vibration signal analysis and processing. In order to achieve a rolling bearing state (normal, inner ring, rolling body, outer ring fault and not the same degree of degradation) intelligent assessment methods. Based on the method of vibration signal feature extraction and reduction deeply on the optimization problem of further research on the hyper sphere structure of multi class support vector machine and evaluation model which is a key technical problem of how to establish the multi state of rolling bearing. The main work includes: (1) based on ensemble empirical mode decomposition (EEMD) method the vibration signal time-frequency analysis. Comparative analysis verified that the EEMD Hilbert spectrum time-frequency analysis method with time and frequency resolution based on the characteristics of anti mode mixing. And for EEMD decomposition When the problem of selecting add noise amplitude and the average number of this set of 2 important parameters, standard deviation from the energy point of view, adding white noise of the EEMD method for intrinsic mode function criterion; vibration signal of rolling bearing was improved after the EEMD decomposition (IMF) still contain false component and is not sensitive to the rolling bearing fault component IMF, a kurtosis value IMF drawing algorithm combined with the normalized correlation coefficient. Experimental results verify the effectiveness of the proposed method, for further extraction and lay a solid foundation. (2) research on feature extraction and reduction method of vibration signal of multi domain features. In order to characterize the running state of rolling bearing, reflect the global features of the rolling bearing vibration signal and local characteristics, research on multi domain feature extraction in time domain, frequency domain and time-frequency domain. The time domain and frequency domain feature extraction are analyzed. To improve the EEMD sensitive IMF respectively based on the time domain index, frequency index, autoregressive model and singular value decomposition method. The structure of the eigenvector matrix feature vector of the rolling bearing and the single sample based on the state, and the establishment of the State Library of rolling bearing. According to the existing characteristics of relevance and redundancy between the high dimension problem study, manifold learning algorithm, combined with support vector machine (SVM) through experimental analysis to determine the most effective method of rolling bearing feature reduction. (3) research on intelligent classification method and intelligent fault diagnosis methods. Hyper sphere multi class SVM structure has a series of advantages, but its classification accuracy is compared with ordinary SVM high. To solve this problem, the research of classification rules, the improvement scheme is proposed, and a new decision criteria of key areas. At the same time, according to the empirical determination of hyper sphere multi class SVM nuclear structure parameter range The problem, the calculation formula of super sphere distance, the distance between the center of the proposed optimal separation as the index to determine the kernel parameters selection, to reduce the training time consumption. The further study of the intelligent fault diagnosis method of rolling bearings in different working state. A multi class SVM intelligent diagnosis model of hyper sphere structure and a large number of experiments, verify the effectiveness of the proposed method. (4) research on the evaluation method of rolling bearing. According to the bearing fault intelligent diagnosing method can determine fault state of affiliation, cannot be quantified description of damage and failure in order to change the performance state of the quantitative evaluation, from the principle SVM classification, analysis of vibration propagation mechanism of rolling bearing structure and sensor position, put forward the evaluation index of SVM compensation based on relative distance from the modified sphere; The structure of multi class SVM principle, characteristic vector direction, of the state sphere in many aspects, and put forward the evaluation index of cosine distance compensation generalized minimum distance, the establishment of intelligent evaluation model. Through the rolling bearing vibration data and incomplete life complete data on two aspects of the experimental research, comparative analysis of the performance the evaluation of the model.

【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：TH133.33;TN911.7

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