【摘要】：乏信息即貧信息,指研究對象呈現(xiàn)的特征信息不完備或不充分,缺乏先驗知識。在低碳經(jīng)濟背景下,齒輪傳動逐漸向輕量化、高功率密度方向發(fā)展,其面臨的強度和可靠性問題更加突出。長期以來,齒輪疲勞性能評價主要依靠經(jīng)典力學(xué)、疲勞壽命試驗,并以威布爾、指數(shù)分布或?qū)?shù)正態(tài)分布為假設(shè),進行可靠性分析;傳統(tǒng)的疲勞試驗分析方法以大樣本經(jīng)典統(tǒng)計理論為基礎(chǔ),不僅需要大量的試驗工作投入,而且人力、物力耗費巨大,發(fā)展相對滯后;隨著新能源、新材料與重載交通運輸機械的快速發(fā)展,動力傳輸機械的極端工況對相關(guān)齒輪的疲勞壽命與可靠性提出了更高的要求,傳統(tǒng)的齒輪疲勞性能評估理論已無法滿足社會的需求,迫使人們不得不面對大量乏信息系統(tǒng)的處理與評價。作者以雙漸開線齒輪為研究對象,基于乏信息理論對該齒輪在隨機振動情況下對接觸疲勞壽命的影響進行研究,具體內(nèi)容如下:(1)將乏信息系統(tǒng)理論引入到雙漸開線齒輪疲勞強度性能試驗評估中,突破以經(jīng)典幾何、力學(xué)理論和大樣本試驗統(tǒng)計為基礎(chǔ)的P-N-S模型�；诜π畔⒗碚�,分別建立灰色系統(tǒng)理論、自助法及蒙特卡洛法的數(shù)學(xué)模型,通過對雙漸開線齒輪參數(shù)進行評估,分析三種模型的特性。(2)結(jié)合灰色GM(1,1)模型和Bootstrap抽樣原理的優(yōu)點,提出一種基于乏信息理論的動態(tài)估計預(yù)測的新方法,并建立灰自助預(yù)測模型。利用MAT LAB軟件對灰自助模型進行可靠度仿真,對比均勻分布、Weibull分布等統(tǒng)計法,分析灰自助模型的可靠性。(3)利用灰自助模型對雙漸開線齒輪的隨機振動誤差進行預(yù)測,并分別與灰色GM(1,1)模型、蒙特卡洛法的預(yù)測值進行對比,確定灰自助模型的可行性與準確性。(4)基于乏信息理論,在灰自助模型中引入峰值概率密度函數(shù)和應(yīng)力功率譜密度法,研究雙漸開線齒輪在隨機振動情況下對接觸疲勞壽命的預(yù)測。(5)利用灰自助模型預(yù)測雙漸開線齒輪軸向、周向及徑向振動過程中疲勞壽命,通過對比試驗數(shù)據(jù)模擬出該模型的誤差分布范圍,分析該模型預(yù)測結(jié)果的準確性,驗證該模型能否解決雙漸開線齒輪乏信息疲勞壽命問題。
[Abstract]:Lack of information, that is, poor information, means that the characteristic information presented by the research object is incomplete or insufficient, and lacks prior knowledge. Under the background of low carbon economy, the gear transmission is developing towards the direction of light weight and high power density, and the problems of strength and reliability are more prominent. For a long time, the fatigue performance evaluation of gears mainly depends on classical mechanics, fatigue life test and reliability analysis based on Weibull, exponential distribution or logarithmic normal distribution. The traditional fatigue test analysis method is based on the classical statistical theory of large sample. It not only needs a large amount of experimental work investment, but also consumes a lot of manpower and material resources, and its development is relatively lagging behind. With the rapid development of new energy, new materials and heavy haul transportation machinery, the extreme working conditions of power transmission machinery require higher fatigue life and reliability of gear. The traditional theory of gear fatigue performance evaluation can not meet the needs of the society, forcing people to face a large number of processing and evaluation of lack of information system. The influence of double involute gear on contact fatigue life under random vibration is studied based on the theory of lack of information. The main contents are as follows: (1) the theory of lack of information system is introduced into the fatigue strength test evaluation of double involute gear, which breaks through the P-N-S model based on classical geometry, mechanics theory and large sample test statistics. Based on the theory of lacking information, the mathematical models of grey system theory, self-help method and Monte Carlo method are established, and the parameters of double involute gear are evaluated. The characteristics of the three models are analyzed. (2) combining the advantages of the grey GM model and the principle of Bootstrap sampling, a new method of dynamic estimation and prediction based on the lack of information theory is proposed, and the grey self-help prediction model is established. The reliability of the grey self-help model is simulated by MAT LAB software, and the reliability of the grey self-help model is analyzed by comparing the statistical methods such as uniform distribution and Weibull distribution. (3) the random vibration error of double involute gear is predicted by using the grey self-help model. And compared with the grey GM (1) model and Monte Carlo method, the feasibility and accuracy of the grey self-help model are determined. (4) based on the lack of information theory, The peak probability density function and the stress power spectrum density method are introduced into the grey self-help model to study the prediction of contact fatigue life of double involute gears under random vibration. (5) the grey self-help model is used to predict the axial direction of double involute gears. In the process of cyclic and radial vibration, the error distribution range of the model is simulated by comparing the experimental data, the accuracy of the prediction result is analyzed, and it is verified that the model can solve the problem of fatigue life of double involute gear without information.
【學(xué)位授予單位】：青島科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TH132.41

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