本文選題：滾動(dòng)軸承 + 無(wú)失效數(shù)據(jù)�。� 參考：《中國(guó)計(jì)量學(xué)院》2013年碩士論文

【摘要】：隨著滾動(dòng)軸承生產(chǎn)制造水平的提高，其壽命也有了很大的提高，在可靠性試驗(yàn)過(guò)程中，會(huì)出現(xiàn)大量的無(wú)失效數(shù)據(jù)。特別在一些高成本、高可靠性的滾動(dòng)軸承如風(fēng)電軸承、高速列車軸承等的可靠性試驗(yàn)中，一般會(huì)選擇小樣本、無(wú)失效截尾試驗(yàn)。在此情況下，由于缺少失效信息，傳統(tǒng)的評(píng)價(jià)方法，如GB24607-2009規(guī)定的最佳線性無(wú)偏估計(jì)等不能對(duì)滾動(dòng)軸承的可靠性進(jìn)行適當(dāng)?shù)脑u(píng)價(jià)。針對(duì)以上問(wèn)題，必須研究新的評(píng)價(jià)方法。本文在前人研究的基礎(chǔ)上，應(yīng)用Bayes方法，對(duì)上述問(wèn)題進(jìn)行深入的理論和仿真研究，提出了兩種適合于無(wú)失效數(shù)據(jù)情況下的滾動(dòng)軸承可靠性評(píng)價(jià)方法及其模型。 第一章，闡述了滾動(dòng)軸承壽命試驗(yàn)及可靠性評(píng)價(jià)的重要意義，討論了滾動(dòng)軸承壽命分布--Weibull分布相關(guān)參數(shù)和可靠性指標(biāo)，介紹了課題來(lái)源，在此基礎(chǔ)上提出了論文的研究?jī)?nèi)容與研究重點(diǎn)。 第二章，闡述了滾動(dòng)軸承壽命的定義、試驗(yàn)原理、壽命與計(jì)算模型，論述了滾動(dòng)軸承可靠性評(píng)價(jià)的基本理論與方法，討論了Weibull分布模型的可靠度、失效概率、失效率、可靠壽命等指標(biāo)間的關(guān)系。最后討論了Weibull分布形狀參數(shù)的幾何意義和物理意義以及Bayes理論和Bayes統(tǒng)計(jì)模型。 第三章，提出了基于虛擬信息構(gòu)建的評(píng)價(jià)方法及模型。在Weibull分布無(wú)失效數(shù)據(jù)下，在每個(gè)截尾時(shí)間點(diǎn)的可靠度估計(jì)過(guò)程中引入前一個(gè)截尾時(shí)間點(diǎn)無(wú)失效樣本的虛擬失效信息，使得對(duì)該截尾時(shí)間點(diǎn)的可靠度估計(jì)具有更高的可信度與更好的穩(wěn)健性。在得出每個(gè)截尾時(shí)間點(diǎn)可靠度的估計(jì)值后通過(guò)加權(quán)最小二乘法得出Weibull分布的兩個(gè)參數(shù)。實(shí)例計(jì)算表明，當(dāng)可靠度先驗(yàn)分布中的超參數(shù)在一定的區(qū)間變化時(shí)，本文提出的方法比其它方法具有更好的穩(wěn)健性。 第四章，提出了基于擬合Weibull分布形狀參數(shù)歷史數(shù)據(jù)作為先驗(yàn)信息的評(píng)價(jià)方法與模型。根據(jù)形狀參數(shù)的歷史試驗(yàn)數(shù)據(jù)，擬合出形狀參數(shù)的概率分布作為先驗(yàn)信息。將Weibull分布轉(zhuǎn)化為指數(shù)分布，根據(jù)共軛先驗(yàn)分布構(gòu)造原則構(gòu)造出指數(shù)分布中失效率的先驗(yàn)信息。然后，以失效率和形狀參數(shù)為切入點(diǎn)，結(jié)合無(wú)失效試驗(yàn)數(shù)據(jù)，得出失效率和形狀參數(shù)的Bayes估計(jì)，進(jìn)而計(jì)算出Weibull分布的特征壽命的估計(jì)。最后通過(guò)一組實(shí)例來(lái)驗(yàn)證估計(jì)結(jié)果的準(zhǔn)確性，并討論估計(jì)的穩(wěn)健性。 第五章，基于Matlab GUI，編制了滾動(dòng)軸承可靠性評(píng)價(jià)軟件。本軟件利用本文的方法對(duì)滾動(dòng)軸承的可靠性作出評(píng)價(jià)，，該評(píng)價(jià)系統(tǒng)主要由3個(gè)功能模塊組成：Bayes估計(jì)模塊，其中包括本文第三章提出的基于虛擬信息的Bayes估計(jì)方法、第四章提出的形狀參數(shù)先驗(yàn)分布分別為均勻分布和Weibull分布的兩種估計(jì)方法、茆詩(shī)松估計(jì)方法與吳來(lái)林估計(jì)方法共5種方法；Bayes估計(jì)穩(wěn)定性評(píng)價(jià)模塊，主要用于計(jì)算截尾時(shí)間變化時(shí)，以上5種估計(jì)方法的穩(wěn)定性；形狀參數(shù)的先驗(yàn)分布擬合模塊，此模塊會(huì)根據(jù)輸入的數(shù)據(jù)判斷出形狀參數(shù)最符合Weibull分布、正態(tài)分布、和指數(shù)分布中的哪種分布，然后擬合出最佳分布。 第六章，對(duì)全文的研究方法與研究結(jié)果進(jìn)行了總結(jié)，提出了未來(lái)的研究方向與本研究的不足之處。
[Abstract]:With the improvement of the rolling bearing production and manufacturing level, its life has also been greatly improved. In the process of reliability test, there will be a lot of no failure data. Especially in the reliability test of some high cost and high reliability rolling bearings, such as wind power bearing and high speed train bearing, the small sample will be selected and no failure truncation test is made. In this case, due to the lack of failure information, the traditional evaluation method, such as the optimal linear unbiased estimation of GB24607-2009, can not properly evaluate the reliability of rolling bearings. In view of the above problems, a new evaluation method must be studied. Based on the previous research, the Bayes method is applied to the above problems. Based on the theory and simulation research, two reliability evaluation methods and models for rolling bearing are presented, which are suitable for the case of zero failure data.
In the first chapter, the important significance of the rolling bearing life test and reliability evaluation is expounded. The related parameters and reliability indexes of the --Weibull distribution of the rolling bearing life distribution are discussed, and the source of the subject is introduced. On this basis, the research content and the research emphasis of the paper are put forward.
In the second chapter, the definition of the life of rolling bearing, the principle of the test, the life and the calculation model, the basic theory and method of the reliability evaluation of the rolling bearing are discussed, and the relationship between the reliability, the failure probability, the inefficiency and the reliable life of the Weibull distribution model is discussed. Finally, the geometric meaning of the shape parameter of the Weibull distribution is discussed and the geometric meaning of the shape parameter is discussed. The physical meaning and the Bayes theory and the Bayes statistical model.
In the third chapter, the evaluation method and model based on virtual information construction is proposed. Under the Weibull distribution without failure data, the virtual failure information of the previous truncated time point without failure samples is introduced in the reliability estimation process of each truncated time point, which makes the reliability estimation of the truncated time point have higher reliability and better reliability. Robustness. After estimating the reliability of each truncated time point, two parameters of the Weibull distribution are obtained by the weighted least square method. The example calculation shows that the proposed method has better robustness than the other methods when the super parameters in the reliability prior distribution vary in a certain interval.
In the fourth chapter, the evaluation method and model based on the historical data of fitting Weibull distribution shape parameters as a priori information are proposed. According to the historical data of the shape parameters, the probability distribution of the shape parameters is fitted as a priori information. The Weibull distribution is transformed into an exponential distribution, and the index is constructed according to the principle of the conjugate prior distribution. A priori information of the failure rate in the cloth is given. Then, with the failure rate and shape parameters as the entry point, the Bayes estimation of the loss of efficiency and shape parameters is obtained by combining with the non failure test data. Then the estimation of the characteristic life of the Weibull distribution is calculated. Finally, a set of examples is used to verify the accuracy of the estimation of the results, and the robustness of the estimation is also discussed.
In the fifth chapter, based on Matlab GUI, the reliability evaluation software of rolling bearing is developed. This software uses this method to evaluate the reliability of rolling bearings. The evaluation system mainly consists of 3 functional modules: Bayes estimation module, including the Bayes estimation method based on virtual information proposed in the third chapter of this paper, and the fourth chapter The prior distribution of shape parameters is two estimation methods of uniform distribution and Weibull distribution respectively. There are 5 methods in the estimation method and the Wu Lailin estimation method, and the Bayes estimation stability evaluation module is mainly used to calculate the stability of the above 5 estimation methods when the truncated time changes are calculated, and the prior distribution of shape parameters is fitted to the module, According to the input data, this module will determine the shape parameters which are most consistent with the Weibull distribution, normal distribution, and which distribution in the exponential distribution, and then fit the best distribution.
The sixth chapter summarizes the research methods and results of the full text, and points out the future research directions and shortcomings of this research.
【學(xué)位授予單位】：中國(guó)計(jì)量學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TH133.33

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