Forecasting Method for Product Reliability Along with Performance Data
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The existing reliability theory is based on current knowledge of probability distributions and trends of product performances. This article proposes a forecasting method of the product reliability along with the performance data, without any prior information on probability distributions and trends. Fusing an evaluating indicator with five chaotic forecasting methods, five runtime data of the future performance are predicted by current performance data. Via the bootstrap, many generated runtime data along with the performance data are gained, and the predicted reliability function of the product runtime can therefore be established. The experimental investigation on the rolling bearing friction torque shows that the calculated values are in very good accordance with the measured values.
KeywordsReliability Product performance Runtime Time series Chaotic forecasting Information-poor system
This project was funded by the National Natural Science Foundation of China (Grant No. 51075123) and the Natural Science Research Project of the Education Department of Henan Province (Grant No. 2010B460008), and the Doctoral Scientific Research Initiation Fund of Henan University of Science and Technology (Grant No. 09001318).
- 1.Sihem, L., Wassila, K., Malek, B.: Transmitter reliability and receiver performance trade-off analysis for transparent satellite communication systems. ICIC Express Lett. 3(3), 247–252 (2009)Google Scholar
- 6.Mani, S., Ehsan, H., Peyman, F.: Real time reliability study of a model with increasing failure rates. Appl. Mech. Mater. 110–116, 2774–2779 (2012)Google Scholar
- 11.Zhang, J.-P., Wang, R.-T.: Reliability life prediction of VFD by constant temperature stress accelerated life tests and maximum likelihood estimation. J. Test. Eval. 37(4), 316–320 (2009)Google Scholar
- 12.Wang, T., Wu, J., Zhang, H.Y., Dai, C.: Reliability design of valve spring based on grey theory. Adv. Inf. Sci. Serv. Sci. 3(8), 219–225 (2011)Google Scholar
- 13.Xia, X.T., Lv, T.M., Meng, F.N.: Gray chaos evaluation model for prediction of rolling bearing friction torque. J. Test. Eval. 38(3), 291–300 (2010)Google Scholar
- 16.Lv, J.H., Lu, J.A., Chen, S.H.: Analysis and Application of Chaos Time Series. Wuhan University Press, Wuhan (2002). (in Chinese)Google Scholar
- 17.Lv, J.H., Zhang, S.C.: Application of adding-weight one-rank local-region method in electric power system short-term load forecast. Control Theory Appl. 19(5), 767–770 (2003). (in Chinese)Google Scholar
- 28.Kantz, H., Shreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (1997)Google Scholar
- 29.Meng, Q.F., Peng, Y.H.: Improved adding weight first order local prediction method for chaotic time series. Comput. Eng. Appl. 43(35), 61–64 (2007). (in Chinese)Google Scholar
- 30.Zuo, J., Wang, H., Zeng, Z.F.: An improved model of add-weighted one-rank local-region multi-steps forecasting. Stat. Decis. 24(6), 33–34 (2008). (in Chinese)Google Scholar