An exploratory study for predicting component reliability with new load conditions

Zhengwei HU, Xiaoping DU

PDF(224 KB)
PDF(224 KB)
Front. Mech. Eng. ›› 2019, Vol. 14 ›› Issue (1) : 76-84. DOI: 10.1007/s11465-018-0522-x
RESEARCH ARTICLE
RESEARCH ARTICLE

An exploratory study for predicting component reliability with new load conditions

Author information +
History +

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 re-evaluate 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 maxi- mum and minimum values. The case study shows that good accuracy can be obtained even though the new reliability is provided with the aforementioned interval.

Keywords

reliability / component / failure mode / prediction / random variable

Cite this article

Download citation ▾
Zhengwei HU, Xiaoping DU. An exploratory study for predicting component reliability with new load conditions. Front. Mech. Eng., 2019, 14(1): 76‒84 https://doi.org/10.1007/s11465-018-0522-x

References

[1]
Johnson D G, Genco N, Saunders M N, An experimental investigation of the effectiveness of empathic experience design for innovative concept generation. Journal of Mechanical Design, 2014, 136(5): 051009
CrossRef Google scholar
[2]
Saunders M N, Seepersad C C, Hölttä-Otto K. The characteristics of innovative, mechanical products. Journal of Mechanical Design, 2011, 133(2): 021009
CrossRef Google scholar
[3]
Chan J, Fu K, Schunn C, On the benefits and pitfalls of analogies for innovative design: Ideation performance based on analogical distance, commonness, and modality of examples. Journal of Mechanical Design, 2011, 133(8): 081004
CrossRef Google scholar
[4]
Kleinschmidt E J, Cooper R G. The impact of product innovativeness on performance. Journal of Product Innovation Management, 1991, 8(4): 240–251
CrossRef Google scholar
[5]
Cruse T A. Reliability-Based Mechanical Design. Volume 108. Boca Raton: CRC Press, 1997
[6]
Lawless J. Statistical methods in reliability. Technometrics, 1983, 25(4): 305–316
CrossRef Google scholar
[7]
Nikolaidis E, Ghiocel D M, Singhal S. Engineering Design Reliability Handbook. Boca Raton: CRC Press, 2004
[8]
Choi S K, Grandhi R V, Canfield R A. Reliability-Based Structural Design. New York: Springer, 2006
[9]
Chiralaksanakul A, Mahadevan S. First-order approximation methods in reliability-based design optimization. Journal of Mechanical Design, 2005, 127(5): 851–857
CrossRef Google scholar
[10]
Du X, Sudjianto A. First order saddlepoint approximation for reliability analysis. AIAA Journal, 2004, 42(6): 1199–1207
CrossRef Google scholar
[11]
Zhao Y G, Ono T. A general procedure for first/second-order reliability method (FORM/SORM). Structural Safety, 1999, 21(2): 95–112
CrossRef Google scholar
[12]
Mahadevan S. Monte Carlo Simulation. New York: Marcel Dekker, 1997, 123–146
[13]
Cheng Y, Du X. System reliability analysis with dependent component failures during early design stage—A feasibility study. Journal of Mechanical Design, 2016, 138(5): 051405
CrossRef Google scholar
[14]
Hu Z, Du X. System reliability prediction with shared load and unknown component design details. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2017, 31(3): 223–234
CrossRef Google scholar
[15]
Hu Z, Du X. A physics-based reliability method for components adopted in new series systems. In: Proceedings of 2016 Annual Reliability and Maintainability Symposium (RAMS). Tucson: IEEE, 2016, 1–7
CrossRef Google scholar
[16]
Resende L C, Manso L A, Dutra W D, Support vector machine application in composite reliability assessment. In: Proceedings of 18th the International Conference on Intelligent System Application to Power Systems (ISAP). IEEE, 2015, 1–6
[17]
Hastie T, Tibshirani R, Friedman J. Overview of Supervised Learning. New York: Springer, 2009, 9–41
[18]
Schölkopf B. Statistical Learning and Kernel Methods. New York: Springer, 2001, 3–24
[19]
Hu Z, Du X. System reliability analysis with in-house and outsourced components. In: Proceedings of the 2nd International Conference on System Reliability and Safety (ICSRS). IEEE, 2017, 146–150
[20]
Hu Z, Du X. Integration of statistics- and physics-based methods—A feasibility study on accurate system reliability prediction. Journal of Mechanical Design, 2018, 140(7): 074501–074507
CrossRef Google scholar
[21]
Lipson C, Sheth N J, Disney R L. Reliability Prediction-Mechanical Stress/Strength Interference. DTIC Document, 1967
[22]
Zhang J, Ma X, Zhao Y. A stress-strength time-varying correlation interference model for structural reliability analysis using copulas. IEEE Transactions on Reliability, 2017, 66(2): 351–365
CrossRef Google scholar
[23]
Zhang X, Yang J, Thomas R. Mechanization outsourcing clusters and division of labor in Chinese agriculture. China Economic Review, 2017, 43: 184–195
CrossRef Google scholar
[24]
Huang C J, Jiang J C. Research of smartphone industry outsourcing decision model. Journal of Information and Optimization Sciences, 2018, 39(3): 725–737
CrossRef Google scholar
[25]
Eggert A, Böhm E, Cramer C. Business service outsourcing in manufacturing firms: An event study. Journal of Service Management, 2017, 28(3): 476–498
CrossRef Google scholar
[26]
Whitcomb P J, Anderson M J. RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments. Boca Raton: CRC press, 2004

Acknowledgements

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.

RIGHTS & PERMISSIONS

2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature
AI Summary AI Mindmap
PDF(224 KB)

Accesses

Citations

Detail

Sections
Recommended

/