Remaining Useful Life Estimation of Stochastically Deteriorating Feedback Control Systems with a Random Environment and Impact of Prognostic Result on the Maintenance Process
The objective and originality of this work are twofold. On one hand, it considers the degradation modeling and Remaining Useful Life (RUL) estimation for the closed-loop dynamic systems, which have not been addressed extensively in the literature. On the other hand, the paper examines how the prognosis result impacts the maintenance process. Indeed, due to their natural ageing and/or non desired effects of the operating condition, actuators deal with the loss of effectiveness which is a source of performance degradation of closedloop system. In this paper, we consider a control system with classical Proportional-Integral-Derivative controller and stochastically deteriorating actuator. It is assumed that the actuators are subject to shocks that occur randomly in time.
An integrated model is proposed which jointly describes the states of the controlled process and the actuators degradation. The RUL can be estimated by a probabilistic approach which consists of two steps. First, the system state regarding the available information is estimated online by Particle Filtering method. Then, the RUL of the system is estimated by Monte Carlo simulation. To illustrate the approach and highlight the impact of the prognosis result on the maintenance process, a well-known simulated tank level control system is used. The maintenance decision rule is based on the quantiles of RUL histogram. In order to evaluate the performance of the maintenance policy, a cost model is developed.
How to Cite
Remaining useful Life, predictive maintenance, device degradation, stochastic filtering, closed-loop systems
Astr¨om, K. J., & H¨agglund, T. (1995). Pid controllers: theory, design and tuning (2nd ed.). Research Triangle Park.
Brandejsky, A., De Saporta, B., Dufour, F., & Elegbede, C. (2011). Numerical method for the distribution of a service time. In Proceedings of the Conference ESREL 2011.
Chen, H.-M., & Chen, Z.-Y. (2008). Implement of a cascade integral sliding mode controller for a water tank level control system. In Innovative computing information and control, 2008. icicic’08. 3rd international conference on (p. 162-162).
Chiquet, J., Limnios, N., & Eid, M. (2009). Piecewise deterministic markov processes applied to fatigue crack growth modelling. Journal of Statistical Planning and Inference, 139(5), 1657 - 1667.
Cocozza-Thivent, C. (2011). Processus de renouvellement markovien, processus de markov déterministes par morceaux. Online book available on the webpage: http://perso-math.univmlv.fr/users/cocozza. christiane/recherchepageperso/PresentationRMetPDMP.html.
Davis, M. H. (1993). Markov models and optimization (Vol. 49). Chapman and Hall. Dieulle, L., Bérenguer, C., Grall, A., & Roussignol, M. (2003). Sequential condition-based maintenance scheduling for a deteriorating system. European Journal of Operational Research, 150(2), 451 - 461.
Doucet, A., & Johansen, A. M. (2009). A tutorial on particle filtering and smoothing: fifteen years later. Handbook of Nonlinear Filtering, 656-704. Do Van, P., Levrat, E., Voisin, A., Iung, B., et al. (2012). Remaining useful life (rul) based maintenance decision making for deteriorating systems. In 2nd ifac workshop on advanced maintenance engineering, service and technology (a-mest’12).
Huynh, K. T., Barros, A., & Bérenguer, C. (2012). Adaptive condition-based maintenance decision framework for deteriorating systems operating under variable environment and uncertain condition monitoring. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 226(6), 602–623.
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483-1510.
Khoury, E., Deloux, E., Grall, A., & Berenguer, C. (2013). On the use of time-limited information for maintenance decision support: A predictive approach under maintenance constraints. Mathematical Problems in Engineering, 2013.
Kitagawa, G. (1996). Monte carlo filter and smoother for non-gaussian nonlinear state space models. Journal of computational and graphical statistics, 5(1), 1-25.
Lorton, A., Fouladirad, M., & Grall, A. (2013). A methodology for probabilistic model-based prognosis. European Journal of Operational Research, 225(3), 443–454.
Nguyen, D. N., Dieulle, L., & Grall, A. (2013). A deterioration model for feedback control systems with random environment. In Proceedings of the Conference ESREL 2013.
Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: a review. The International Journal of Advanced Manufacturing Technology, 50(1-4), 297-313.
Si, X.-S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011). Remaining useful life estimation: A review on the statistical data driven approaches. European Journal of Operational Research, 213(1), 1 - 14.
Sikorska, J., Hodkiewicz, M., & Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5), 1803 - 1836.
Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems. Wiley.
Van Noortwijk, J. (2009). A survey of the application of gamma processes in maintenance. Reliability Engineering & System Safety, 94(1), 2-21.
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