Imprecise reliability assessment for heavy numerical control machine tools against small sample size problem

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• 作者：Zheng Liu 刘 征 ; Yanfeng Li 李彦锋…
• 关键词：reliability assessment ; natural extension model ; empirical probability distribution ; machine tool spindle
• 刊名：Journal of Shanghai Jiaotong University (Science)
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：21
• 期：5
• 页码：605-610
• 全文大小：325 KB
• 刊物类别：Engineering
• 刊物主题：Electrical Engineering
  Life Sciences
  Architecture
  Chinese Library of Science
  
• 出版者：Shanghai Jiao Tong University Press
• ISSN：1995-8188
• 卷排序：21

文摘

Small sample size problem is one of the main problems that heavy numerical control (NC) machine tools encounter in their reliability assessment. In order to deal with the small sample size problem, many indirect reliability data such as reliability data of similar products, expert opinion, and engineers’ experience are used in reliability assessment. However, the existing mathematical theories cannot simultaneously process the above reliability data of multiple types, and thus imprecise probability theory is introduced. Imprecise probability theory can simultaneously process multiple reliability data by quantifying multiple uncertainties (stochastic uncertainty, fuzzy uncertainty, epistemic uncertainty, etc.) together. Although imprecise probability theory has so many advantages, the existing natural extension models are complex and the computation result is imprecise. Therefore, they need some improvement for the better application of reliability engineering. This paper proposes an improved imprecise reliability assessment method by introducing empirical probability distributions to natural extension model, and the improved natural extension model is applied to the reliability assessment of heavy NC machine tool spindle to illustrate its effectiveness.