Input variables used in mechanical system analysis or Prognostics and Health Management (PHM) are various, such as life of material, size and property of product. It has uncertainties (Aleatory uncertainty) due to various noises like temperature, mechanical error and various environmental differences during experiments in real field. For example, obtained data by conducting repeatedly the experiment in the identical environment condition have to be same theoretically. However, experimental data have the variation due to the generated error or noise by the various factors and uncertainties. In this study, the improved algorithm how to determine proper distribution of input variable containing uncertainties is proposed using the probabilistic and statistical method. Also, the validity of this improved algorithm is verified using the automotive engine mounts rubber stiffness data.
Wand, M.P., & Jones, M.C. (1994). Kernel Smoothing. CRC press
Yoojeong Noh. (2016). A Comparison Study on Statistical Modeling Methods. Journal of the Korea Academia- Industrial Cooperation Society. vol. 17, issue. 5, pp. 645-652, doi: 10.5762/KAIS.2016.17.5.645
Yoojeong Noh, Young-Jin Kang, O-Kaung Lim. (2015). Integrated Statistical Modeling approach of Uncertainty variables. Proceedings of the KSME 2015 Fall Annual Meeting. pp. 431-432.
Lee, S-Y., Song X-Y. (2012). Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences. United Kingdom, John Wiley & Sons Ltd.