本文選題：齒輪傳動系統(tǒng)　切入點(diǎn)：重分配小波尺度譜　出處：《西安建筑科技大學(xué)》2014年博士論文

【摘要】：齒輪箱是一種量大面廣的機(jī)械設(shè)備關(guān)鍵性基礎(chǔ)部件,也是最易損壞的零部件之一,其運(yùn)行狀況直接影響到整個(gè)機(jī)器或機(jī)組設(shè)備的安全運(yùn)行,因此,如何能盡早發(fā)現(xiàn)齒輪系統(tǒng)的早期故障,做到合理組織安排設(shè)備的維修,避免發(fā)生重大安全事故,造成重大的經(jīng)濟(jì)損失具有重大意義。機(jī)械設(shè)備的振動信號蘊(yùn)含著系統(tǒng)(正常、故障)狀態(tài)的信息,各種類型故障也有一定的規(guī)律可循,因此,采用振動信號對大型、關(guān)鍵機(jī)組運(yùn)行狀態(tài)監(jiān)測和故障診斷是目前設(shè)備管理維護(hù)的主要手段。由于受齒輪傳動振動響應(yīng)和環(huán)境噪聲的影響,齒輪早期故障的微弱信號往往被其他成分或環(huán)境噪聲淹沒,故障信號具有復(fù)雜的非線性、非平穩(wěn)特性,采用傳統(tǒng)的基于平穩(wěn)信號假設(shè)的信號處理方法很難對其取得準(zhǔn)確診斷,因此,研究有效去噪、消噪信號預(yù)處理技術(shù)和非平穩(wěn)信號處理方法對設(shè)備故障診斷具有非常大的意義。小波分析和經(jīng)驗(yàn)?zāi)B(tài)分解(EMD)是近年來發(fā)展起來的兩種處理非平穩(wěn)信號的時(shí)頻方法,小波閾值去噪,形態(tài)濾波,奇異值分解技術(shù)是幾種應(yīng)用較多的去噪方法,兩種時(shí)頻方法與幾種去噪方法相融合,被廣泛應(yīng)用于信號檢測,機(jī)械故障診斷等工程領(lǐng)域。同時(shí),隨著設(shè)備向著高速度、高功率、高可靠性、大型化/微型、智能化、集成化的方向發(fā)展,使得傳統(tǒng)的設(shè)備故障診斷方法和單一智能診斷方法已不能完全滿足設(shè)備狀態(tài)復(fù)雜性的需求,將多種智能診斷方法相融合的智能診斷技術(shù)是目前研究的熱點(diǎn)方向。因此,本文對齒輪系統(tǒng)動力學(xué)和故障形成機(jī)理、小波分析理論、小波閾值去噪和重分配小波譜奇異值去噪、Hilbert-Huang變換理論、D-S證據(jù)理論、遺傳算法-BP神經(jīng)網(wǎng)絡(luò),模糊優(yōu)化理論研究的基礎(chǔ)上,提出了基于D-S證據(jù)理論的多模型融合齒輪早期故障智能診斷方法,分析齒輪典型故障信號的結(jié)果驗(yàn)證了該方法的有效性。對齒輪故障診斷提供了依據(jù)。本文主要工作如下:[1]本文建立了考慮摩擦、時(shí)變剛度、齒側(cè)間隙的具有偏心直齒輪摩擦-間隙齒輪振動模型,分析考慮摩擦、齒側(cè)間隙、偏心質(zhì)量時(shí)的齒輪動力學(xué)行為以及它們的頻譜特征。[2]提出了基于shannon熵優(yōu)化TBP參數(shù)的重分配小波尺度譜進(jìn)行SVD降噪方法,通過仿真信號分析發(fā)現(xiàn)該方法具有比小波尺度譜、重分配小波尺度譜更好的時(shí)頻聚集性,且其時(shí)頻分辨率能夠同時(shí)實(shí)現(xiàn)最佳,具有更高的時(shí)頻分布可讀性。因此,該方法能夠識別出強(qiáng)噪聲背景下的機(jī)械早期故障微弱信號成分,為強(qiáng)噪聲背景下機(jī)械早期故障微弱信號的去噪、消噪以及特征提取和故障診斷奠定了一定的理論基礎(chǔ)。[3]將經(jīng)驗(yàn)?zāi)Ｊ椒纸?EMD)方法和分形維數(shù)融合,提出了基于小波閾值去噪和EMD分形融合故障診斷方法,列出了基于EMD的分形維數(shù)的具體步驟。并將該方法應(yīng)用于齒輪傳動齒面磨損、斷齒故障狀態(tài)振動信號的故障診斷中,用關(guān)聯(lián)維數(shù)均方根值替代關(guān)聯(lián)維數(shù),實(shí)現(xiàn)對齒輪齒面磨損和斷齒等故障的準(zhǔn)確診斷,取得了良好的效果。[4]提出了基于D-S證據(jù)理論的多模型融合智能齒輪故障診斷方法,通過實(shí)例驗(yàn)證:本文提出的多模型融合模型能夠綜合利用各單一智能模型的優(yōu)點(diǎn),使得區(qū)分度比單一模型有明顯提高,即使單一模型出現(xiàn)誤判,該融合模型仍然能夠得到正確的診斷結(jié)果。具有較好的容錯性、糾錯性。
[Abstract]:The gear box is the key basic parts of machinery and equipment of a large amount of wide, one of the most easily damaged parts, its operation conditions directly affect the safe operation of the machine or equipment. Therefore, how to find fault gear system as soon as possible, to achieve a reasonable arrangement of equipment maintenance, to avoid the occurrence of major security the accident caused significant economic losses is of great significance. The vibration signals of mechanical equipment contains system (normal, fault) state information, various types of fault has certain rules, therefore, the vibration signal of the large, key unit condition monitoring and fault diagnosis are the main means of equipment management and maintenance. Because of influence of gear vibration and noise, weak signal early gear failure is often other components or environmental noise, the fault signal is complex Complex nonlinear, non-stationary characteristics, using the traditional signal processing method based on the assumption of stationary signals it is difficult to obtain accurate diagnosis, therefore, research on effective denoising, denoising signal preprocessing techniques and non-stationary signal processing method has great significance for fault diagnosis. Wavelet analysis and empirical mode decomposition (EMD two) is developed in recent years, processing non-stationary time-frequency method, wavelet threshold denoising, morphological filtering, singular value decomposition technique is widely used in several denoising methods, two kinds of time and frequency of several denoising method of integration, has been widely used in signal detection, fault diagnosis etc. engineering. At the same time, along with the equipment towards high speed, high power, high reliability, large / miniature, intelligent, integrated direction, making the equipment fault diagnosis method and the traditional single intelligent diagnosis method has not been able to Fully meet the equipment requirement of complexity, the intelligent diagnosis technology combining intelligent diagnosis methods is the current research focus. Therefore, the gear system dynamics and fault formation mechanism, the theory of wavelet analysis, wavelet threshold de-noising and reassigned wavelet singular value spectrum denoising, Hilbert-Huang transform theory, D-S theory of evidence. -BP neural network, genetic algorithm, fuzzy optimization theory, a multi model D-S evidence theory fusion incipient fault diagnosis method based on the analysis of typical fault signals of gear results verify the effectiveness of the proposed method. Provide the basis for gear fault diagnosis. The main work is as follows: This paper established [1] friction, time-varying stiffness, friction with eccentric gear clearance gear vibration model of gear backlash, considering friction, backlash, eccentric quality The gear dynamic behavior and their spectral characteristics.[2] proposed reassigned wavelet scale Shannon entropy optimization TBP parameter spectrum denoising method based on SVD, the simulation signal analysis shows that this method is better than wavelet scalogram and reassigned scalogram better time-frequency aggregation, and the time-frequency resolution can also achieve the best. Has the time-frequency distribution more readable. Therefore, the method can identify early fault weak signal components under strong background noise, as strong noise background machinery early fault signal denoising, denoising and feature extraction and fault diagnosis has laid a theoretical foundation of the.[3] (empirical mode decomposition EMD) fusion method and the fractal dimension, the wavelet threshold denoising and EMD fractal fusion fault diagnosis method based on the list of specific steps of fractal dimension based on EMD and the party. Method is applied to the gear tooth wear, broken tooth fault diagnosis fault vibration signal, using the correlation dimension of RMS value instead of correlation dimension, realize the accurate diagnosis of gear tooth wear and broken tooth fault, achieved good results with the.[4] proposed model D-S evidence theory fusion method for gear fault diagnosis based on intelligent, proved that this multi model fusion model can take advantage of the single integrated intelligent model, the discrimination is better than a single model, even a single model of misjudgment, the fusion model can still obtain the correct diagnosis result. With fault tolerance, good error correction.

【學(xué)位授予單位】：西安建筑科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TH132.41
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本文編號：1692140