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Assessment method of heavy NC machine reliability based on Bayes theory

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Abstract

It is difficult to collect the prior information for small-sample machinery products when their reliability is assessed by using Bayes method. In this study, an improved Bayes method with gradient reliability (GR) results as prior information was proposed to solve the problem. A certain type of heavy NC boring and milling machine was considered as the research subject, and its reliability model was established on the basis of its functional and structural characteristics and working principle. According to the stress-intensity interference theory and the reliability model theory, the GR results of the host machine and its key components were obtained. Then the GR results were deemed as prior information to estimate the probabilistic reliability (PR) of the spindle box, the column and the host machine in the present method. The comparative studies demonstrated that the improved Bayes method was applicable in the reliability assessment of heavy NC machine tools.

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Authors and Affiliations

  1. Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300072, China

    Lei Zhang  (张 雷) & Taiyong Wang  (王太勇)

  2. Tianjin NC System Engineering Center, Tianjin University, Tianjin, 300072, China

    Lei Zhang  (张 雷) & Taiyong Wang  (王太勇)

  3. College of Mechanical Engineering, Yanshan University, Qinhuangdao, 066004, China

    Zhanqi Hu  (胡占齐)

Authors
  1. Lei Zhang  (张 雷)
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  2. Taiyong Wang  (王太勇)
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  3. Zhanqi Hu  (胡占齐)
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Corresponding author

Correspondence to Taiyong Wang  (王太勇).

Additional information

Supported by the National Science and Technology Major Project of China (No. 2009ZX04002-061), the National Science and Technology Support Program (No. 2013BAF06B00) and the Natural Science Foundation of Tianjin (No. 13JCZDJC34000).

Zhang Lei, born in 1987, male, doctorate student.

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Zhang, L., Wang, T. & Hu, Z. Assessment method of heavy NC machine reliability based on Bayes theory. Trans. Tianjin Univ. 22, 105–109 (2016). https://doi.org/10.1007/s12209-016-2567-4

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  • DOI: https://doi.org/10.1007/s12209-016-2567-4

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