An Inference-based Prognostic Framework for Health Management of Automotive Systems



Published Nov 11, 2020
Chaitanya Sankavaram Anuradha Kodali Krishna Pattipati Satnam Singh Yilu Zhang Mutasim Salman


This paper presents a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic time-series data. The framework employs proportional hazards model and a soft dynamic multiple fault diagnosis algorithm for inferring the degraded state trajectories of components and to estimate their remaining useful life times. The framework takes into account the cross-subsystem fault propagation, a case prevalent in any networked and embedded system. The key idea is to use Cox proportional hazards model to estimate the survival functions of error codes and symptoms (probabilistic test outcomes/prognostic indicators) from failure time data and static parameter data, and use them to infer the survival functions of components via soft dynamic multiple fault diagnosis algorithm. The average remaining useful life and its higher-order central moments (e.g., variance, skewness, kurtosis) can be estimated from these component survival functions. The framework is demonstrated on datasets derived from two automotive systems, namely hybrid electric vehicle regenerative braking system, and an electronic throttle control subsystem simulator. Although the proposed framework is validated on automotive systems, it has the potential to be applicable to a wide variety of systems, ranging from aerospace systems to buildings to power grids.

Abstract 142 | PDF Downloads 237



applications: automotive, Data-driven and model-based prognostics, inference, multiple fault diagnosis

Adams, D. E. (2002). Nonlinear damage models for diagnosis and prognosis in structural dynamic systems. In SPIE Conference Proceedings, vol. 4733, pp. 1-12.
Bishop, C. M. (1997). Neural Networks for Pattern Recognition. Clarendon Press, Oxford.
Byington, C. S., Watson, M., Edwards, D. and Stoelting, P. (2004). A model-based approach to prognostics and health management for flight control actuators. Proceedings of IEEE Aerospace Conference, Vol. 6, pp. 3551.
Celaya, J. R., Saxena, A., and Goebel, K. (2012). Uncertainty representation and interpretation in model-based prognostics algorithms based on kalman filter estimation. NASA Ames Research Center, Moffett Field, CA.
Chelidze, D., Cusumano, J. P., & Chatterjee, A. (2002). Dynamical systems approach to damage evolution tracking, part I: The experimental method. Journal of Vibration and Acoustics, vol. 124, pp. 250-257.
Chiang, L. H., Russel, E., & Braatz, R. (2001). Fault detection and diagnosis in industrial systems. London: Springer-Verlag.
Coble, J. B. (2010). Merging data sources to predict remaining useful life – an automated method to identify prognostic parameters, PhD dissertation, University of Tennessee.
Cox, D. R., & Oakes, D. (1984). Analysis of survival data. Chapman and Hall.
Daigle, M. J., and Goebel, K (2011). A model-based prognostics approach applied to pneumatic valves. International Journal of Prognostics and Health Management, vol. 2: 84.
Duda, R. O., Hart, P. E., & Stork, D. (2000). Pattern classification. John Wiley & Sons, New York.
Ehsani, M., Gao, Y., and Emadi, A. (2009). Modern electric, hybrid electric, and fuel cell vehicles: fundamentals, theory, and design. CRC press.
Environmental Protection Agency (EPA). Dynamometer Drive Schedules. [Online]. Available: [Accessed: 02-Jul-2015].
Garga, A. K., Mcclintic, K. T., Campbell, R. L., Yang, C. C., & Lebold, M. S. (2001). Hybrid reasoning for prognostic learning in cbm systems. Proceedings of IEEE Aerospace Conference, pp. 2957-2969.
Gebraeel, N. and Lawley, M. A. (2008). A neural network degradation model for computing and updating residual life distributions. IEEE Trans. Automation Science and Engineering, vol. 5, no. 1, pp. 154–163.
Goebel, K., Saha, B., and Saxena, A. (2008). A comparison of three data-driven techniques for prognostics. 62nd meeting of the society for machinery failure prevention technology (MFPT), pp. 119-131.
Gorjian, N., Ma, L., Mittinty, M., Yarlagadda, P., Sun Y. (2009a). A review on reliability models with covariates. Proceedings of the 4th World Congress on Engineering Asset Management, Athens, Greece, September.
Gorjian, N., Ma, L., Mittinty, M., Yarlagadda, P., Sun Y. (2009b). A review on degradation models in reliability analysis. In: Proceedings of the 4th World Congress on Engineering Asset Management, Athens, Greece, September.
Jardine, A. K. S., & Tsang, A. H. C. (2005). Maintenance, Replacement, and Reliability: Theory and Applications, New York: CRC Press.
Klabfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data 2nd edition, New York, Wiley.
Klein, J. P., & Moeschberger, M. L. (2003). Survival Analysis Techniques for Censored and Truncated Data. Springer-Verlag, New York.
Kodali, A., Ponizovskaya-Devine, E., Robinson, P., Luchinsky, D., Bajwa, A., Khasin, M., Perotti, J, and Brown, B. (2015). D-matrix based fault modeling for Cryogenic loading systems. Annual Conference of the Prognostics and Health Management Society.
Kumar, S., Torres, M., Chan, Y. C., & Pecht, M. (2008) A hybrid prognostics methodology for electronic products. IEEE International Joint Conference on Neural Networks.
Lall, P., Pecht, M., and Harkim, E. (1997). Influence of Temperature on Microelectronics and System Reliability, New York: CRC Press.
Luo. J., Namburu, M., Pattipati, K., Qiao, L., Kawamoto, M., & Chigusa, S. (2003). Model-based prognostic techniques. IEEE Autotestcon Conference, pp. 330-340.
Ma, Z., & Krings, A. W. (2008). Survival analysis approach to reliability, survivability and Prognostics and Health Management (PHM). IEEE Aerospace Conference, May.
Metzler, D., Lavrenko, V., & Croft, W. B. (2004). Formal Multiple Bernoulli Models for Language Modeling. Proceedings of 27th Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR’ 04), Sheffield, UK, pp. 540-541.
Murthy, D. N. P., Xie, M., & Jiang, R. (2004). Weibull Models, New York: Wiley.
Pattipati, K., & Alexandridis, M. G. (1988). Application of heuristic search and information theory to sequential fault diagnosis. IEEE International Symposium on Intelligent Control, pp. 291-296, August.
Pearl, J. (1986). Fusion, propagation and structuring in belief networks. Artificial Intelligence, Vol. 29, No. 3, pp. 241-288.
Pecht, M., Das, D., and Ramakrishnan, A. (2002). The IEEE standards on Reliability Program and Reliability Prediction methods for Electronic Equipment,” Microelectronic Reliability, pp. 1259-1266.
Ray, A. and Tangirala, S. (1996). Stochastic modeling of fatigue crack dynamics for on-line failure prognostics. IEEE Transactions on Control Systems Technology, Vol.4, pp. 443-451.
Sankavaram, C., Kodali, A., Pattipati, K., Wang, B., Azam, M., and Singh, S. (2011). A Prognostic Framework for Health Management of Coupled Systems. IEEE International Conference on Prognostics and Health Management, Denver, CO, June.
Sankavaram, C., Pattipati, B., Pattipati, K., Zhang, Y., and Howell, M. (2014) “Fault Diagnosis in Hybrid Electric Vehicle Regenerative Braking System,” IEEE Access, vol. 2, pp. 1225-1239, October.
Schwabacher, M. A. (2005). A survey of data-driven prognostics. Infotech@Aerospace, American Institute of Aeronautics and Astronautics 2005-7002, September, Arlington, Virginia.
Serrao, L., Onori, S., Rizzoni, G., and Guezennec, Y. (2009). A novel modelbased algorithm for battery prognosis. 7th IFAC SAFEPROCESS.
Si, X., Wang, W., Hu, C., and Zhou, D. (2011) Remaining useful life estimation – A review on the statistical data driven approaches. European Journal of Operational Research, Vol. 213, No. 1, pp 1-14.
Singh, S., Kodali, A., & Pattipati, K. (2009a). A factorial hidden Markov model-based reasoner for diagnosing multiple intermittent faults. IEEE CASE, Bangalore, August.
Singh, S., Kodali, A., Choi, K., Pattipati, K., Namburu, S. M., Chigusa, S., Prokhorov, D. V., & Qiao, L. (2009b). Dynamic Multiple Fault Diagnosis Problem Formulations and Solution Techniques. IEEE Trans. on Systems, Man and Cybernetics: Part A, vol. 39, no. 1, pp. 160-176, January.
Singh, S., Holland, S. W., & Bandyopadhyay, P. (2010). Trends in the development of system-level fault dependency models. IEEE Aerospace Conference, March.
Swanson, D. (2001). A general prognostic tracking algorithm for predictive maintenance. Proceedings of IEEE International Conference on Aerospace, vol.6, pp. 2971-2977.
Vichare, N., Rodgers, P., Eveloy, V., and Pecht, M (2004). In situ temperature measurement of a notebook computer – A case study of health and usage monitoring of electronics. IEEE trans. On Device and Materials Reliability, pp. 658 – 663, December.
Vlok, P. J., Coetzee, J. L., Banjevic, D., Jardine, A. K. S., & Makis, V. (2002). Optimal Component Replacement Decisions Using Vibration Monitoring and the PHM. Journal of the Operational Research Society, Vol. 53, 193-202.
Wang, P., & Vachtsevanos, G. (1999). Fault prognosis using dynamic wavelet neural networks. in Maintenance and Reliability Conference (MARCON 99), May.
Technical Papers