本文選題：滾動軸承 + 無失效數(shù)據(jù)��； 參考：《中國計量學院》2013年碩士論文

【摘要】：隨著滾動軸承生產(chǎn)制造水平的提高，其壽命也有了很大的提高，在可靠性試驗過程中，會出現(xiàn)大量的無失效數(shù)據(jù)。特別在一些高成本、高可靠性的滾動軸承如風電軸承、高速列車軸承等的可靠性試驗中，一般會選擇小樣本、無失效截尾試驗。在此情況下，由于缺少失效信息，傳統(tǒng)的評價方法，如GB24607-2009規(guī)定的最佳線性無偏估計等不能對滾動軸承的可靠性進行適當?shù)脑u價。針對以上問題，必須研究新的評價方法。本文在前人研究的基礎上，應用Bayes方法，對上述問題進行深入的理論和仿真研究，提出了兩種適合于無失效數(shù)據(jù)情況下的滾動軸承可靠性評價方法及其模型。 第一章，闡述了滾動軸承壽命試驗及可靠性評價的重要意義，討論了滾動軸承壽命分布--Weibull分布相關參數(shù)和可靠性指標，介紹了課題來源，在此基礎上提出了論文的研究內(nèi)容與研究重點。 第二章，闡述了滾動軸承壽命的定義、試驗原理、壽命與計算模型，論述了滾動軸承可靠性評價的基本理論與方法，討論了Weibull分布模型的可靠度、失效概率、失效率、可靠壽命等指標間的關系。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)計模型。 第三章，提出了基于虛擬信息構(gòu)建的評價方法及模型。在Weibull分布無失效數(shù)據(jù)下，在每個截尾時間點的可靠度估計過程中引入前一個截尾時間點無失效樣本的虛擬失效信息，使得對該截尾時間點的可靠度估計具有更高的可信度與更好的穩(wěn)健性。在得出每個截尾時間點可靠度的估計值后通過加權(quán)最小二乘法得出Weibull分布的兩個參數(shù)。實例計算表明，當可靠度先驗分布中的超參數(shù)在一定的區(qū)間變化時，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)作為先驗信息的評價方法與模型。根據(jù)形狀參數(shù)的歷史試驗數(shù)據(jù)，擬合出形狀參數(shù)的概率分布作為先驗信息。將Weibull分布轉(zhuǎn)化為指數(shù)分布，根據(jù)共軛先驗分布構(gòu)造原則構(gòu)造出指數(shù)分布中失效率的先驗信息。然后，以失效率和形狀參數(shù)為切入點，結(jié)合無失效試驗數(shù)據(jù)，得出失效率和形狀參數(shù)的Bayes估計，進而計算出Weibull分布的特征壽命的估計。最后通過一組實例來驗證估計結(jié)果的準確性，并討論估計的穩(wěn)健性。 第五章，基于Matlab GUI，編制了滾動軸承可靠性評價軟件。本軟件利用本文的方法對滾動軸承的可靠性作出評價，，該評價系統(tǒng)主要由3個功能模塊組成：Bayes估計模塊，其中包括本文第三章提出的基于虛擬信息的Bayes估計方法、第四章提出的形狀參數(shù)先驗分布分別為均勻分布和Weibull分布的兩種估計方法、茆詩松估計方法與吳來林估計方法共5種方法；Bayes估計穩(wěn)定性評價模塊，主要用于計算截尾時間變化時，以上5種估計方法的穩(wěn)定性；形狀參數(shù)的先驗分布擬合模塊，此模塊會根據(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，對全文的研究方法與研究結(jié)果進行了總結(jié)，提出了未來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.
【學位授予單位】：中國計量學院
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TH133.33

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