A Microscopic Approach for Generic Degradation Modeling



Published Nov 13, 2020
Farhang Momeni Xiaoning Jin Jun Ni


One of the main challenges in prognostics is degradation modeling for which there are no straightforward methods compared with system governing equations modeling (e.g., Newton`s law, Euler`s law, thermodynamics conservation laws, fluid dynamics laws, and so forth). Once both governing equations and degradation equations are generated, then the RUL or EOL can be easily estimated by using a filtering technique like Kalman filter. This paper presents a new approach for generic degradation modeling which can be engaged in (1) complex engineering systems, and (2) the structures which are fabricated by the new manufacturing processes such as 4D printing that in both cases the physical knowledge is not adequate to model the degradation equations. In the existing approaches for parametric degradation modeling, there are always possibilities that a specific degradation phenomenon of a new system is ignored. This deficiency arises from the assumption made in the previous studies that degradation phenomenon is equivalently represented by degradation mechanism such as crack, wear, corrosion, erosion, and so forth. Here we relax this assumption and provide a more general approach for parametric degradation modeling. This research first, quantifies the concepts of governing equations and degradation equations in prognostics in a general view, second provides a generic approach for deriving the governing equations by using techniques of system identification, third, it digs into the concepts of degradation and provides a microscopic approach for generic degradation modeling, and finally it combines and incorporates the governing equations and degradation equations in a way that the generality is conserved. In addition, this paper opens the door of degradation modeling in 4D printing by starting from the conceptual point of view to the final generic methodology.

Abstract 125 | PDF Downloads 140



PHM, Data-driven prognostics, system-level prognostics, physics-based prognostics, Degradation Mechanisms, microscopic degradation modeling, Degradation phenomenoa, 4D printing

Aizpurua, J. I., & Catterson, V. M. (2015). Towards a Methodology for Design of Prognostic Systems. In Annual Conference of the Prognostics and Health Management Society, Coronado, California, USA. Available: https://www.phmsociety.org/sites/phmsociety.org/files/phm_submission/2015/phmc_15_022.pdf
Borutzky, W. (2014). Failure Prognosis for Hybrid Systems Based on ARR Residuals. Bond Graph Model-based Fault Diagnosis of Hybrid Systems, 221-233. doi:10.1007/978-3-319-11860-4_9.
Daigle, M. J., & Goebel, K. (2011). A model-based prognostics approach applied to pneumatic valves. International Journal of Prognostics and Health Management Volume 2 (color), 84. Available: http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20120002692.pdf
Daigle, M., Saha, B., & Goebel, K. (2012). A comparison of filter-based approaches for model-based prognostics. In Aerospace Conference, 2012 IEEE, 1-10. doi:10.1109/aero.2012.6187363.
Garnier, H., Mensler, M., & Richard, A. (2003). Continuous-time model identification from sampled data: Implementation issues and performance evaluation. International Journal of Control, 76(13), 1337-1357. doi:10.1080/0020717031000149636.
Liao, L., & Kottig, F. (2014). Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction. IEEE Transactions on Reliability IEEE Trans. Rel., 63(1), 191-207. doi:10.1109/tr.2014.2299152.
Ljung, L. (1999). System Identification: theory for the user. Upper Saddle River, NJ: Prentice Hall PTR.
Ljung, L. (2009). Experiments with Identification of Continuous Time Models. IFAC Proceedings Volumes, 42(10), 1175-1180. doi:10.3182/20090706-3-fr-2004.00195.
Ljung, L. (2013). System Identification Toolbox: User’s Guide. The Mathworks Inc., Natick, MA. [Online]. Available:www.mathworks.com/help/pdf_doc/ident/ident.pdf.
Medjaher, K., & Zerhouni, N. (2009). Residual-based failure prognostic in dynamic systems. IFAC Proceedings Volumes, 42(8), 716-721. doi:10.3182/20090630-4-es-2003.00119.
Peysson, F., Ouladsine, M., Noura, H., Leger, J., & Allemand, C. (2008). New Approach to Prognostic System Failures. IFAC Proceedings Volumes, 41(2), 12861-12866. doi:10.3182/20080706-5-kr-1001.02175.
Peysson, F., Ouladsine, M., Outbib, R., Leger, J., Myx, O., & Allemand, C. (2009). A Generic Prognostic Methodology Using Damage Trajectory Models. IEEE Transactions on Reliability IEEE Trans. Rel., 58(2), 277-285. doi:10.1109/tr.2009.2020123.
Raviv, D., Zhao, W., McKnelly, C., Papadopoulou, A., Kadambi, A., Shi, B., Hirsch, S., Dikovsky, D., Zyracki, M., Olguin, C., Raskar, R., & Tibbits, S. (2014). Active Printed Materials for Complex Self-Evolving Deformations. Sci. Rep. Scientific Reports, 4, 7422. doi:10.1038/srep07422.
Tibbits, S. (2014). 4D Printing: Multi-Material Shape Change. Architectural Design Archit Design, 84(1), 116-121. doi:10.1002/ad.1710
Tibbits, S., McKnelly, C., Olguin, C., Dikovsky, D., Hirsch, S. (2014). 4D Printing and Universal Transformation. Proceedings of the Association for Computer Aided Design in Architecture 2014 Los Angeles, CA, 539-548. Available: http://papers.cumincad.org/data/works/att/acadia14_539.content.pdf