Similar articles

Abstract: In this paper, an integrated validation method and process are developed for multivariate dynamic systems. The principal component analysis approach is used to address multivariate correlation and dimensionality reduction, the dynamic time warping and correlation coefficient are used for error assessment, and the subject matter experts (SMEs)’ opinions and principal component analysis coefficients are incorporated to provide the overall rating of the dynamic system. The proposed method and process are successfully demonstrated through a vehicle dynamic system problem.

[1]Capitani, P., Ciaccia, P., 2007. Warping the time on data streams. Data & Knowledge Engineering, 62(3):438-458.

[2]Ferson, S., Oberkampf, W.L., Ginzburg, L., 2008. Model validation and predictive capability for the thermal challenge problem. Computer Methods in Applied Mechanics and Engineering, 197(29-32):2408-2430.

[3]Fu, Y., Jiang, X., Yang, R.J., 2009. Auto-Correlation of an Occupant Restraint System Model Using a Bayesian Validation Metric. SAE World Congress, Detroit, USA, SAE 2009-01-1402.

[4]Fu, Y., Zhan, Z.F., Yang, R.J., 2010. A Study of Model Validation Method for Dynamic Systems. SAE World Congress, Detroit, USA, SAE 2010-01-0419.

[5]Hotelling, H., 1933. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6):417-441.

[6]Jiang, X., Mahadevan, S., 2008. Bayesian wavelet method for multivariate model assessment of dynamical systems. Journal of Sound and Vibration, 312(4-5):694-712.

[7]Jiang, X., Yang, R.J., Barbat, S., Weerappuli, P., 2009. Bayesian probabilistic PCA approach for model validation of dynamic systems. SAE International Journal of Materials & Manufacturing, 2(1):555-563.

[8]Joliffe, I.T., 2002. Principal Component Analysis. Springer, New York, USA.

[9]Lei, H., Govindaraju, V., 2003. Synchronization of Batch Trajectory Based on Multi-Scale Dynamic Time Warping. Proceedings of the Second International Conference on Machine Learning and Cybernetics, Xi’an, China.

[10]Mahadevan, S., Rebba, R., 2005. Validation of reliability computational models using Bayes networks. Reliability Engineering and System Safety, 87(2):223-232.

[11]Oberkampf, W.L., Barone, M.F., 2006. Measures of agreement between computation and experiment: validation metrics. Journal of Computational Physics, 217(1):5-36.

[12]Oberkampf, W.L., Trucano, T.G., 2006. Design of and Comparison with Verification and Validation Benchmarks. Technical Report Sand No. 2006-5376C, Sandia National Laboratories, Albuquerque, New Mexico, USA.

[13]Rabiner, L.R., Huang, B.H., 1993. Fundamentals of Speech Recognition, Prentice Hall.

[14]Rebba, R., Mahadevan, S., 2006. Model predictive capability assessment under uncertainty. AIAA Journal, 44(10):2376-2384.

[15]Sarin, H., Kokkolaras, M., Hulbert, G., Papalambros, P., Barbat, S., Yang, R.J., 2010. Comparing time histories for validation of simulation models: error measures and metrics. Journal of Dynamic Systems Measurement and Control, 132(6):061401.

[16]Schwer, L.E., 2007. Validation metrics for response histories: perspectives and case studies. Engineering with Computers, 23(4):295-309.

[17]Zhan, Z.F., Fu, Y., Yang, R.J., Peng, Y.H., 2011a. An enhanced Bayesian based model validation method for dynamic systems. Journal of Mechanical Design, 133(4):041005.

[18]Zhan, Z.F., Fu, Y., Yang, R.J., 2011b. Enhanced Error Assessment of Response Time Histories (EEARTH) Metric and Calibration Process. SAE World Congress, Detroit, USA, SAE 2011-01-0245.

[19]Zhan, Z.F., Fu, Y., Yang, R.J., Peng, Y.H., 2011c. An automatic model calibration method for occupant restraint systems. Structural and Multidisciplinary Optimization, 44(6):815-822.