Applied Research on Reliability Evaluation of CNC Machine Tools Based on D-S Evidence Theory

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Abstract:

Reliability is most important to the CNC machine tools and reliability estimation is a very important part of the reliability which has magnificence to allocate resources and put forward scientific policy. Reliability evaluation of computer numerical control machine tools can use all sorts of effective information to decrease the size of test samples and save the development costs and shorten the production cycle. The paper put forward to use D-S evidence theory and the information of experts system to decrease the uncertainty of the reliability evaluation of computer numerical control machine tools. The results show that the method can effectively decrease the uncertainty of the reliability evaluation of computer numerical control machine tools.

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645-649

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November 2013

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Wang Hua-wei. Zhou Jing-lun. He Zu-yu and Sha Chang-ji. Applied Research on Reliability Evaluation of Complicated Systems Based on D-S Evidence Theory[J]. Reliability Engineering. 2003, 3. P116-118.

DOI: 10.1109/icmlc.2002.1174527

Google Scholar

[2] Walls L. Quigley J. Building prior distribution to support Bayesian reliability growth modeling using expert judgement [J]. Reliability Engineering and System Safety, 2007, 74. P117-128.

DOI: 10.1016/s0951-8320(01)00069-2

Google Scholar

[3] Zhang Wen-xiu and Liang Yi. The Principle of Uncertainty Reasoning[M]. Xi'an. Xi'an Jiaotong University Press. (1994).

Google Scholar

[4] Duan Xin-sheng. Evidential Reasoning, Decision and Artificial Intelligence[M]. Beijing. Renmin University of China Press. (1993).

Google Scholar

[5] Fang Yong. The research of multisource data fusion technique in the analysis of remote sensing images[D]. Paper of the institute of surveying and mapping, Zhengzhou. (1998).

Google Scholar

[6] Ehler G. Multisource classification of remote sensing data: fusion of Landsat TM and SAR Images[J]. IEEE T-GRS, 1994. 32(4).

DOI: 10.1109/36.298006

Google Scholar