作者: Pan Xiong, Shuan Li Yuan, Shao Jie Cheng

摘要: The distribution of observation errors is determined according to their magnitudes by using the distribution collocation test method or figure method taking into account the result, sample total, the interval density etc. It is therefore difficult to get the specific type of error distribution of observations by conventional methods. In analyzing the actual situation of the observation error distribution using their statistical properties, this paper proposes the use of unsymmetrical distribution to express the true distribution of the observation errors. The P-norm distribution is a generalized form of a group of error distributions, and from the statistical properties of random errors we can arrive at an unsymmetrical P-norm distribution according to the practical situation of the occurrence of random errors. The common P-norm distribution is the specific case of this distribution. This paper deduces the density function equation of the unsymmetrical P-norm distribution, obtained the statistical properties of the distribution function and the evaluation of precision index. By choosing appropriate value for p, we can get closer to the distribution function of the true error distribution.

1153

作者: Ying Fan, Shun Kun Wang, Feng Zhou, Zhi Cheng Tian, Guang Shuai Ding

摘要: It is difficult to identify distribution types and to estimate parameters of the distribution for small sample censored data when you deal with mechanical equipment reliability analysis. Here, an intelligent distribution identification model was established based on statistical learning theory and the algorithm of multi-element classifier of Support Vector Machine (SVM), and also applied to parameter estimation of small sample censored data, in order to improve the precision of traditional method. Firstly, the algorithm of training based on SVM and the RBF kernel function was selected; secondly, the parameters of the distributions characteristics were drawn; on the basis of these conditions, the distributions identification model and the parameter estimation model were finally constructed. And the model was verified with Monte Carlo simulation method. The results indicate that the new algorithm has more preferable performance in distribution type identification and parameter estimation than the traditional methods.

31

作者: Zhen Yu Feng, Ke Yi Mao, Tian Chun Zou

摘要: In this paper, composite static strength test are analyzed with the Weibull and normal statistical method. Compare the Weibull analysis with the normal analysis, which shows that the A-basis value and B-basis value from the Weibull analysis respectively are less than ones from normal analysis by approximate 9.7% and 2.6% under tension loading, on the other hand they are decreased by approximate 16.1% and 5.2% under compression loading. This analysis result has the higher application worth.

930

作者: Chang Gao Xia, Meng Zhang, Xiang Gao, Zhen Yu Zhang

摘要: The mixed distribution model and the maximum entropy model are used to represent service load of the vehicle clutch. The parameters of those models are estimated with different methods. The findings indicate that maximum entropy distribution can accurately describe different statistical features of random variables as minimally prejudiced probability distribution if order of the distribution function is properly selected, and that the mixed Weibull distribution shows super performance of the complicated statistical model expression. The parameters of those models are estimated by optimization based on non-linear least squares.

5244

作者: Zhao Jun Yang, Wei Wang, Fei Chen, Kai Wang, Xiao Bing Li, Yin Kai Wang

摘要: By using the information entropy theory, a solution to Weibull-small sample prior distribution of system reliability is proposed, which aims at solving the reliability estimation of high-end CNC. Firstly, the prior information is converted from subsystem level into system level based on entropy theory. Then, the prior distribution is solved with the constrained maximum entropy method. Finally, multi-information is fused based on the entropy weighs. It is proved by a case example that this method can obtained the prior distribution under Webull-small sample effectively.

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