An Exploratory Study for Predicting Component Reliability with New Load Conditions
Abstract
Reliability is important to design innovation. A new product should be not only innovative, but also reliable. For many existing components used in the new product, their reliability will change because the applied loads are different from the ones for which the components are originally designed and manufactured. Then the new reliability must be re-evaluated. The system designers of the new product, however, may not have enough information to perform this task. With a beam problem as a case study, this study explores a feasible way to reevaluate the component reliability with new loads given the following information: The original reliability of the component with respect to the component loads and the distributions of the new component loads. Physics-based methods are employed to build the equivalent component limit-state function that can predict the component failure under the new loads. Since the information is limited, the re-evaluated component reliability is given by its maximum and minimum values. The case study shows that good accuracy can be obtained even though the new reliability is provided with the aforementioned interval.
Recommended Citation
Z. Hu and X. Du, "An Exploratory Study for Predicting Component Reliability with New Load Conditions," Frontiers of Mechanical Engineering, vol. 14, no. 1, pp. 76 - 84, Higher Education Press, Mar 2019.
The definitive version is available at https://doi.org/10.1007/s11465-018-0522-x
Department(s)
Mechanical and Aerospace Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Component; Failure Mode; Prediction; Random Variable; Reliability
International Standard Serial Number (ISSN)
2095-0233,; 2095-0241
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 Higher Education Press, All rights reserved.
Publication Date
01 Mar 2019
Comments
This study is based upon work supported by the National Science Foundation (Grant No. CMMI 1562593) and Intelligent Systems Center at Missouri University of Science and Technology.