【摘要】：齒輪箱是一種應(yīng)用面較廣的基礎(chǔ)性傳動(dòng)部件,主要用于改變轉(zhuǎn)速和傳遞動(dòng)力。由于其本身結(jié)構(gòu)復(fù)雜,工作環(huán)境惡劣等原因,齒輪箱容易出現(xiàn)故障。因此,對(duì)齒輪箱進(jìn)行疲勞損傷監(jiān)測(cè)與診斷是非常必要的。基于振動(dòng)信號(hào)的齒輪箱故障特征提取,其主要任務(wù)是從采集到的信號(hào)中提取可用的故障特征信息,前提條件是準(zhǔn)確分離故障振動(dòng)信號(hào),根本目的是提取故障特征信息。本文是基于斜齒輪臺(tái)架疲勞試驗(yàn),以采集到的斜齒輪整個(gè)疲勞壽命周期內(nèi)的實(shí)時(shí)振動(dòng)信號(hào)為研究對(duì)象,提出有效的振動(dòng)信號(hào)降噪方法和反映齒輪損傷程度的特征指標(biāo),并將特征指標(biāo)的變化趨勢(shì)應(yīng)用到斜齒輪的疲勞損傷過(guò)程的分析中。本文首先以斜齒輪單齒嚙合的簡(jiǎn)化動(dòng)力學(xué)模型為基礎(chǔ),研究了斜齒輪的振動(dòng)信號(hào)機(jī)理�；谛盘�(hào)機(jī)理,分析了斜齒輪典型故障及其對(duì)應(yīng)的振動(dòng)信號(hào)特征,并回顧了常用的齒輪故障特征指標(biāo)。針對(duì)斜齒輪振動(dòng)信號(hào)的非線性、非平穩(wěn)性的特征,本文提出希爾伯特振動(dòng)分解(Hilbert Vibration Decomposition,HVD)與自回歸濾波器(AR Filter)相結(jié)合的振動(dòng)信號(hào)降噪方法HVD-AR,其中對(duì)HVD的信號(hào)重構(gòu)方法和AR模型階次確定的相干峭度(Correlation Kurtosis,CK)準(zhǔn)則進(jìn)行了重點(diǎn)討論,并通過(guò)實(shí)測(cè)點(diǎn)蝕損傷過(guò)程振動(dòng)信號(hào)包絡(luò)譜驗(yàn)證該降噪方法的有效性。根據(jù)故障對(duì)齒輪振動(dòng)信號(hào)頻譜邊頻帶影響的機(jī)理,將高低階嚙合頻率的邊頻帶分開(kāi)考慮,提出高低階嚙合頻率邊頻帶估計(jì)的兩個(gè)特征指標(biāo)(4和(4,并將其應(yīng)用到實(shí)測(cè)振動(dòng)信號(hào)的分析中�？紤]現(xiàn)有的斜齒輪臺(tái)架試驗(yàn)條件,擬定疲勞壽命試驗(yàn)方案,并對(duì)試驗(yàn)結(jié)果進(jìn)行簡(jiǎn)要分析,同時(shí)獲取整個(gè)壽命過(guò)程的振動(dòng)信號(hào)數(shù)據(jù),為其后的斜齒輪疲勞損傷過(guò)程的分析提供必要的數(shù)據(jù)支撐。采用斜齒輪的典型損傷過(guò)程(齒面點(diǎn)蝕和輪齒斷齒)的全壽命振動(dòng)信號(hào)以數(shù)據(jù)驅(qū)動(dòng)方式驗(yàn)證了HVD-AR信號(hào)降噪方法的有效性,并將特征指標(biāo)(4和(4的變化趨勢(shì)與常用的特征指標(biāo)RMS、峭度值、ER和FM4進(jìn)行對(duì)比分析,驗(yàn)證其對(duì)于斜齒輪損傷過(guò)程分析的實(shí)用性。結(jié)果表明,HVD-AR能夠有效降噪斜齒輪振動(dòng)信號(hào),特征值(4和(4的變化趨勢(shì)能夠有效反映斜齒輪的疲勞損傷過(guò)程,能夠及早發(fā)現(xiàn)早期的微弱故障。
[Abstract]:Gearbox is a kind of basic transmission component with wide application, which is mainly used to change speed and transfer power. The gearbox is prone to malfunction because of its complicated structure and bad working environment. Therefore, it is necessary to monitor and diagnose the fatigue damage of gearbox. The main task of gearbox fault feature extraction based on vibration signal is to extract the available fault feature information from the collected signal. The precondition is to separate the fault vibration signal accurately, and the fundamental purpose is to extract the fault feature information. Based on the fatigue test of helical gear bench, this paper takes the real time vibration signal of helical gear in the whole fatigue life cycle as the research object, and puts forward an effective noise reduction method of vibration signal and characteristic index to reflect the damage degree of gear. The change trend of characteristic index is applied to the analysis of fatigue damage process of helical gear. In this paper, the vibration signal mechanism of helical gear is studied based on the simplified dynamic model of single tooth meshing of helical gear. Based on the signal mechanism, the typical fault of helical gear and its corresponding vibration signal characteristics are analyzed, and the commonly used gear fault characteristic indexes are reviewed. Aiming at the nonlinearity and nonstationarity of helical gear vibration signal, this paper presents a new method, HVD-AR, which combines Hilbert vibration decomposition (Hilbert Vibration Decomposition,HVD) with autoregressive filter (AR Filter). The signal reconstruction method of HVD and the coherent kurtosis (Correlation Kurtosis,CK) criterion determined by the order of AR model are discussed in detail, and the effectiveness of the method is verified by measuring the envelope spectrum of vibration signal in the process of pitting damage. According to the mechanism of the influence of fault on the frequency band of gear vibration signal, the edge frequency band of high and low order meshing frequency is considered separately, and two characteristic indexes of edge band estimation of high and low order meshing frequency are presented (4 and 4, respectively). It is applied to the analysis of measured vibration signal. Considering the existing helical gear bench test conditions, the fatigue life test scheme is drawn up, and the test results are analyzed briefly, and the vibration signal data of the whole life process are obtained at the same time. It provides necessary data support for the analysis of the fatigue damage process of helical gears. Using the lifetime vibration signal of the typical damage process of helical gear (tooth surface pitting and gear tooth breaking), the effectiveness of HVD-AR signal denoising method is verified by data-driven method. The change trend of characteristic index (4 and 4) is compared with RMS, kurtosis, ER and FM4, to verify its practicability for the analysis of helical gear damage process. The results show that HVD-AR can effectively reduce the vibration signal of helical gears, and the variation trends of eigenvalues (4 and 4) can effectively reflect the fatigue damage process of helical gears, and early weak faults can be detected as early as possible.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH132.41

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