Model-based prognostic approaches use first-principle or regression models to estimate and predict the system’s health state in order to determine the remaining useful life (RUL). Then, in order to handle the prediction results uncertainty, the Bayesian framework is usually used, in which the prior estimates are updated by infield measurements without changing the model parameters. Nevertheless, in the case of system-level prognostic, the mere updating of the prior estimates, based on a predetermined model, is no longer sufficient. This is due to the mutual interactions between components that increase the system modeling uncertainties and may lead to an inaccurate prediction of the system RUL (SRUL). Therefore, this paper proposes a new methodology for online joint uncertainty quantification and model estimation based on particle filtering (PF) and gradient descent (GD). In detail, the inoperability input-output model (IIM) is used to characterize system degradations considering interactions between components and effects of the mission profile; and then the inoperability of system components is estimated in a probabilistic manner using PF. In the case of consecutive discrepancy between the prior and posterior estimates of the system health state, GD is used to correct and to adapt the IIM parameters. To illustrate the effectiveness of the proposed methodology and its suitability for an online implementation, the Tennessee Eastman Process is investigated as a case study.
System-level prognostics, Uncertainty quantification, Adaptive model, Online monitoring, Tennessee Eastman Process
Acuna, D. E., & Orchard, M. E. (2017). Particle-filtering- ˜based failure prognosis via Sigma-points: Application to Lithium-ion battery state-of-charge monitoring. Mechanical Systems and Signal Processing, 85, 827 - 848.
Atamuradov, V., Medjaher, K., Dersin, P., Lamoureux, B., & Zerhouni, N. (2017). Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation. International Journal of Prognostics and Health Management, 8(60), 1–31.
Bathelt, A., Ricker, N. L., & Jelali, M. (2015). Revision of the Tennessee Eastman Process model. IFAC (International Federation of Automatic Control) Papers Online, 48(8), 309–314.
Blancke, O., Combette, A., Amyot, N., Komljenovic, D., Levesque, M., Hudon, C., . . . Zerhouni, N. (2018). ´A predictive maintenance approach for complex equipment based on petri net failure mechanism propagation model. In European conference of the prognostics and health management society.
Brahimi, M., Medjaher, K., Leouatni, M., & Zerhouni, N. (2017). Critical components selection for a prognostics and health management system design: An application to an overhead contact system. In Annual conference of the prognostics and health management society.
Cheng, S., Azarian, M., & Pecht, M. (2008). Sensor system selection for prognostics and health monitoring. In International design engineering technical conferences and computers and information in engineering conference) (pp. 1383–1389).
Daigle, M., Bregon, A., & Roychoudhury, I. (2012). A distributed approach to system-level prognostics. In Annual conference of the prognostics and health management society (pp. 71–82).
Das, D., Elburn, E., Pecht, M., & Sood, B. (2019). Evaluating impact of information uncertainties on component reliability assessment. In International reliability physics symposium (irps).
Doucet, A., Godsill, S., & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 197–208.
Downs, J. J., & Vogel, E. F. (1993). A plant-wide industrial process control problem. Computers & Chemical Engineering, 17(3), 245–255.
Gouriveau, R., Medjaher, K., & Zerhouni, N. (2016). From prognostics and health systems management to predictive maintenance 1: Monitoring and prognostics. John Wiley & Sons. doi: 10.1002/9781119371052
Liu, J., & Zio, E. (2016). Dynamic reliability assessment and prognostics with monitored data for multiple dependent degradation components. In European safety and reliability conference.
Mosallam, A., Medjaher, K., & Zerhouni, N. (2015). Component based data-driven prognostics for complex systems: Methodology and applications. In International conference on reliability systems engineering (icrse).
Orchard, M. (2006). A particle filtering-based framework for on-line fault diagnosis and failure prognosis (Unpublished doctoral dissertation). Georgia Institute of Technology, Atlanta, CA.
Orchard, M. E., & Vachtsevanos, G. J. (2009). A particlefiltering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, 31(3-4), 221–246.
Pecht, M. (2009). Prognostics and health management of electronics. In Encyclopedia of structural health monitoring (pp. 263–286). John Wiley & Sons.
Ramasso, E., & Gouriveau, R. (2014). Remaining useful life estimation by classification of predictions based on a neuro-fuzzy system and theory of belief functions. IEEE Transactions on Reliability, 63(2), 555–566. doi:10.1109/TR.2014.2315912
Ribot, P., Pencole, Y., & Combacau, M. (2008). Prognostics ´for the maintenance of distributed systems. In International conference on prognostics and health management.
Rodrigues, L. R. (2018). Remaining useful life prediction for multiple-component systems based on a system-level performance indicator. IEEE/ASME Transactions on Mechatronics, 23(1), 141–150.
Rodrigues, L. R., Gomes, J. P., Ferri, F. A., Medeiros, I. P., Galvao, R. K., & Junior, C. L. N. (2014). Use of ´phm information and system architecture for optimized aircraft maintenance planning. IEEE Systems Journal,9(4), 1197–1207.
Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
Sarih, H., Tchangani, A., Medjaher, K., & Pere, E. (2018). Critical components identification based on experience feedback data in the framework of PHM. IFAC-Papers Online, 51(11), 429–434. (16th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2018)
Savitzky, A., & Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627–1639.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K.(2010). Evaluating prognostics performance for al gorithms incorporating uncertainty estimates. In Ieee aerospace conference (pp. 1–11).
Snyman, J. A., & Wilke, D. N. (2018). Practical mathematical optimization: Basic optimization theory and gradient-based algorithms (Vol. 133). Springer.
Tamssaouet, F., Nguyen, K. T., Medjaher, K., & Orchard, M. (2020). Online joint estimation and prediction for system-level prognostics under component interactions and mission profile effects. ISA Transactions.
Tamssaouet, F., Nguyen, T. P. K., & Medjaher, K. (2019). System-level prognostics under mission profile effects using inoperability input-output model. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1–11.
Tang, L., Orchard, M. E., Goebel, K., & Vachtsevanos, G. (2011). Novel metrics and methodologies for the verification and validation of prognostic algorithms. In Ieee aerospace conference.
Wang, Q., Wang, H., Gupta, C., Rao, A. R., & Khorasgani, H. (2020). A non-linear function-on-function model for regression with time series data.