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.
. [J]. Frontiers of Mechanical Engineering, 2019, 14(1): 76-84.
Zhengwei HU, Xiaoping DU. An exploratory study for predicting component reliability with new load conditions. Front. Mech. Eng., 2019, 14(1): 76-84.
Johnson D G, Genco N, Saunders M N, et al. An experimental investigation of the effectiveness of empathic experience design for innovative concept generation. Journal of Mechanical Design, 2014, 136(5): 051009 https://doi.org/10.1115/1.4026951
2
Saunders M N, Seepersad C C, Hölttä-Otto K. The characteristics of innovative, mechanical products. Journal of Mechanical Design, 2011, 133(2): 021009 https://doi.org/10.1115/1.4003409
3
Chan J, Fu K, Schunn C, et al. 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 https://doi.org/10.1115/1.4004396
4
Kleinschmidt E J, Cooper R G. The impact of product innovativeness on performance. Journal of Product Innovation Management, 1991, 8(4): 240–251 https://doi.org/10.1016/0737-6782(91)90046-2
5
Cruse T A. Reliability-Based Mechanical Design. Volume 108. Boca Raton: CRC Press, 1997
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 https://doi.org/10.1115/1.1899691
10
Du X, Sudjianto A. First order saddlepoint approximation for reliability analysis. AIAA Journal, 2004, 42(6): 1199–1207 https://doi.org/10.2514/1.3877
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 https://doi.org/10.1115/1.4031906
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 https://doi.org/10.1017/S0890060417000130
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 https://doi.org/10.1109/RAMS.2016.7448079
16
Resende L C, Manso L A, Dutra W D, et al. 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 https://doi.org/10.1115/1.4039770
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 https://doi.org/10.1109/TR.2017.2694459
23
Zhang X, Yang J, Thomas R. Mechanization outsourcing clusters and division of labor in Chinese agriculture. China Economic Review, 2017, 43: 184–195 https://doi.org/10.1016/j.chieco.2017.01.012
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 https://doi.org/10.1080/02522667.2018.1424086
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 https://doi.org/10.1108/JOSM-11-2016-0306
26
Whitcomb P J, Anderson M J. RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments. Boca Raton: CRC press, 2004
