Gradual Change Reliability Sensitivity Design for Components with Arbitrary Distribution Parameters Based on Measured Information
- 10.2991/eame-17.2017.41How to use a DOI?
- strength of materials; gradual change reliability; sensitivity analysis; stochastic process models; arbitrary distribution parameters; reliability design
Reliability design of mechanical components mostly focused on establishing pure theoretical mathematical model at present, without involving measured information for working components and gradual characteristics of parameters into theoretical model, which caused some error for reliability design of components. In order to access reliability of existing components correctly, by taking strength of components as a process of independent increments, autocorrelation coefficient of strength is calculated, and effect of loading action and gradual change characteristics of strength is studied, thus a method for computing gradual change reliability is proposed. Combining the reliability design theory with sensitivity analysis method, a numerical method for gradual change reliability sensitivity design of components based on measured information is proposed, and the variation rules of reliability sensitivity of parameters at any moment and effects of design parameters on reliability of components are obtained, which provides the theoretical basis for structural design and life prediction of mechanical components.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xingang Wang AU - Baoyan Wang AU - Miaoxin Chang AU - Mingming Yan PY - 2017/04 DA - 2017/04 TI - Gradual Change Reliability Sensitivity Design for Components with Arbitrary Distribution Parameters Based on Measured Information BT - Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017) PB - Atlantis Press SP - 168 EP - 172 SN - 2352-5401 UR - https://doi.org/10.2991/eame-17.2017.41 DO - 10.2991/eame-17.2017.41 ID - Wang2017/04 ER -