本文選題：加工中心　切入點：可靠性評估　出處：《燕山大學(xué)》2015年碩士論文

【摘要】：可靠性評估技術(shù)是數(shù)控機床可靠性工程技術(shù)的重要組成部分,它可以驗證機床可靠性是否達(dá)到預(yù)期目標(biāo),檢驗機床的設(shè)計是否合理,進而指出機床的薄弱環(huán)節(jié),為改進機床性能提供科學(xué)依據(jù)。但由于高檔數(shù)控機床具有數(shù)量少且故障數(shù)據(jù)匱乏的小樣本特征,傳統(tǒng)的評估方法無法對其可靠性進行有效地評估,這已成為當(dāng)前高檔數(shù)控機床可靠性工作的重點和難點。因此,本文以小樣本的CXK5463車銑加工中心為研究對象,針對傳統(tǒng)貝葉斯方法在利用相似機床信息時確定的先驗分布不合理的問題,通過引入繼承因子,提出了一種混和先驗分布的貝葉斯可靠性評估方法。首先,通過對加工中心的故障數(shù)據(jù)進行再抽樣得到了再生大樣本,并用它與兩型號相似機床的故障數(shù)據(jù)進行相容性檢驗,證實了兩型號相似機床故障數(shù)據(jù)與加工中心現(xiàn)場故障數(shù)據(jù)均相容,得出加工中心服從雙參數(shù)威布爾分布;根據(jù)相似機床的故障數(shù)據(jù)計算得到混合先驗分布中歷史先驗分布參數(shù)的數(shù)學(xué)模型;根據(jù)Reference先驗方法計算得到混合先驗分布中更新先驗分布參數(shù)的數(shù)學(xué)模型。其次,通過對兩型號相似機床故障數(shù)據(jù)和加工中心故障數(shù)據(jù)進行再抽樣處理,分別得到了它們的概率密度分布,進而利用相似機床與加工中心概率密度分布的差異情況計算得到了基于客觀法的繼承因子;通過分析處理兩相似機床與加工中心在各指標(biāo)的相似度專家評分,利用賦權(quán)法計算了各相似指標(biāo)所占權(quán)重,進而根據(jù)權(quán)重和各相似指標(biāo)值計算得到了基于主觀法的繼承因子;利用綜合賦權(quán)法綜合考慮客觀法和主觀法的繼承因子值,得到了綜合繼承因子;根據(jù)信息融合原理將歷史先驗分布、更新先驗分布和繼承因子融合得到了混和先驗分布。然后,根據(jù)貝葉斯公式將混和先驗分布和加工中心現(xiàn)場試驗樣本進行了融合,得到了加工中心故障間隔時間的后驗分布函數(shù)。最后,針對加工中心的后驗分布為多重積分,難以計算的問題,以馬爾科夫鏈蒙特卡羅法為理論基礎(chǔ),運用Open BUGS軟件對加工中心的后驗分布進行了模擬仿真,得到了加工中心MTBF的估計值。通過與傳統(tǒng)貝葉斯方法和無信息貝葉斯方法對比,驗證了本文方法的正確性。
[Abstract]:The reliability evaluation technology is an important part of the reliability engineering technology of NC machine tool, it can verify the reliability of the machine is expected to achieve its objectives, design of test machine is reasonable, and it is pointed out that the weak link of the machine, to provide scientific basis for improvement of the performance of machine tools. But because of the high-end CNC machine tool has the characteristics of small sample quantity is small and the lack of fault data, evaluation the traditional methods can not effectively assess its reliability, which has become the focus and difficulty of the current high-end CNC machine tool reliability. Therefore, this paper takes CXK5463 milling machining center small sample as the research object, according to the traditional Bias method determined a priori information in the use of the similar machine distribution is not reasonable, the inheritance factor introduction, it proposes a hybrid reliability evaluation method of Bias prior distribution. Firstly, based on the machining center Fault data re sampling has been regeneration samples, and use it with two types of fault data of similar machine compatibility test, confirmed the two types of similar machine fault data and processing center field failure data are compatible, the machining center obeys two parameter Weibull distribution; according to the fault data similarity calculated mathematical model of hybrid machine tool the distribution parameters of prior distributions in the prior; according to the Reference method to calculate a priori update mixed prior distribution parameters of the prior distribution mathematical model. Secondly, re sampling processing by similar machine fault data and fault data processing center on the two models, are obtained. The probability density distribution of them, and then use the similar machine tools and machining center the probability density distribution of the difference was calculated based on the method of objective inheritance factor; through the analysis of two phase like Machine tools and machining center in each index of similarity expert score, using weighting method to calculate the similarity index weights, and then according to the weight and the similarity index value is calculated based on the method of subjective factor inheritance; the comprehensive weighting method considering the inheritance factor objective method and subjective value of law, the comprehensive inheritance factor; according to the information fusion principle of history prior distribution, update the prior distribution and inheritance factor fusion has been mixed prior distribution. Then, according to the Bayesian formula mixing field test sample prior distribution and processing center for the integration, has been processing center fault interval posterior distribution function. Finally, the posterior distribution for machining Center for multiple integrals, difficult computational problem, using Markov chain Monte Carlo method as the theoretical basis, using Open BUGS software to the processing center of the posterior distribution. The MTBF value of the machining center is obtained by simulation. The correctness of the method is verified by comparing with the traditional Bias method and the information free Bias method.

【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TG659

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本文編號：1706429