Set-membership methodology for model-based prognosis.

This paper addresses model-based prognosis to predict Remaining Useful Life (RUL) of a class of dynamical systems. The methodology is based on singular perturbed techniques to take into account the slow behavior of degradations. The full-order system is firstly decoupled into slow and fast subsystems. An interval observer is designed for both subsystems under the assumption that the measurement noise and the disturbances are bounded. Then, the degradation is modeled as a polynomial whose parameters are estimated using ellipsoid algorithms. Finally, the RUL is predicted based on an interval evaluation of the degradation model over a time horizon. A numerical example illustrates the proposed technique.

[1]  Hal L. Smith,et al.  Monotone Dynamical Systems: An Introduction To The Theory Of Competitive And Cooperative Systems (Mathematical Surveys And Monographs) By Hal L. Smith , 1995 .

[2]  Y. F. Huang,et al.  On the value of information in system identification - Bounded noise case , 1982, Autom..

[3]  Giovanni Zappa,et al.  Sequential approximation of parameter sets for identification with parametric and nonparametric uncertainty , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[4]  David Chelidze,et al.  Multimode damage tracking and failure prognosis in electromechanical systems , 2002, SPIE Defense + Commercial Sensing.

[5]  Leonid M. Fridman,et al.  Interval estimation for LPV systems applying high order sliding mode techniques , 2012, Autom..

[6]  Tarek Raïssi,et al.  Interval observers design for singularly perturbed systems , 2014, 53rd IEEE Conference on Decision and Control.

[7]  Steven Y. Liang,et al.  Adaptive Prognostics for Rolling Element Bearing Condition , 1999 .

[8]  Ali Saberi,et al.  Quadratic-type Lyapunov functions for singularly perturbed systems , 1981, 1981 20th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[9]  Olivier Bernard,et al.  Interval observers for linear time-invariant systems with disturbances , 2011, Autom..

[10]  J. O'Reilly Full-order observers for a class of singularly perturbed linear time-varying systems , 1979 .

[11]  Olivier Bernard,et al.  Near optimal interval observers bundle for uncertain bioreactors , 2007, 2007 European Control Conference (ECC).

[12]  G. Bastin,et al.  Reduced order dynamical modelling of reaction systems: A singular perturbation approach , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[13]  Mustapha Ouladsine,et al.  A note on unknown input interval observer design with application to systems prognosis , 2011, 2011 9th IEEE International Conference on Control and Automation (ICCA).

[14]  Prodromos Daoutidis,et al.  Model Reduction of Multiple Time Scale Processes in Non-standard Singularly Perturbed Form , 2006 .

[15]  Asok Ray,et al.  Stochastic modeling of fatigue crack dynamics for on-line failure prognostics , 1996, IEEE Trans. Control. Syst. Technol..

[16]  Steven Y. Liang,et al.  STOCHASTIC PROGNOSTICS FOR ROLLING ELEMENT BEARINGS , 2000 .

[17]  Tarek Raïssi,et al.  Robust state estimation for singularly perturbed systems , 2017, Int. J. Control.

[18]  Denis V. Efimov,et al.  Interval observers for continuous-time LPV systems with L1/L2 performance , 2015, Autom..

[19]  Nagi Gebraeel,et al.  Residual life predictions from vibration-based degradation signals: a neural network approach , 2004, IEEE Transactions on Industrial Electronics.

[20]  E. Walter,et al.  Applied Interval Analysis: With Examples in Parameter and State Estimation, Robust Control and Robotics , 2001 .

[21]  Jean-Luc Gouzé,et al.  Closed loop observers bundle for uncertain biotechnological models , 2004 .

[22]  A. Chatterjee,et al.  A Dynamical Systems Approach to Damage Evolution Tracking, Part 1: Description and Experimental Application , 2002 .

[23]  F. Hoppensteadt Properties of solutions of ordinary differential equations with small parameters , 1971 .

[24]  Petar V. Kokotovic,et al.  Singular perturbations and order reduction in control theory - An overview , 1975, at - Automatisierungstechnik.

[25]  J. Gouzé,et al.  Interval observers for uncertain biological systems , 2000 .

[26]  Majid Nayeri,et al.  An interpretable and converging set-membership algorithm , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[27]  Wilfrid Perruquetti,et al.  Design of interval observer for a class of uncertain unobservable nonlinear systems , 2016, Autom..

[28]  Tarek Raïssi,et al.  Set-membership methodology for model-based systems prognosis , 2015 .

[29]  Denis V. Efimov,et al.  Interval State Estimation for a Class of Nonlinear Systems , 2012, IEEE Transactions on Automatic Control.

[30]  Eric Walter,et al.  Guaranteed Nonlinear State Estimator for Cooperative Systems , 2004, Numerical Algorithms.

[31]  Eric Walter,et al.  GUARANTEED NONLINEAR PARAMETER ESTIMATION FOR CONTINUOUS-TIME DYNAMICAL MODELS , 2006 .

[32]  Joe H. Chow,et al.  Singular perturbation and iterative separation of time scales , 1980, Autom..

[33]  Mustapha Ouladsine,et al.  Observer design applied to prognosis of system , 2011, 2011 IEEE Conference on Prognostics and Health Management.

[34]  Joseph P. Cusumano,et al.  A Dynamical Systems Approach to Failure Prognosis , 2004 .

[35]  José Ragot,et al.  State estimation of two-time scale multiple models with unmeasurable premise variables. Application to biological reactors , 2010, 49th IEEE Conference on Decision and Control (CDC).

[36]  F. Mazenc,et al.  Interval observers for discrete‐time systems , 2014 .

[37]  A. Chatterjee,et al.  A Dynamical Systems Approach to Damage Evolution Tracking, Part 2: Model-Based Validation and Physical Interpretation , 2002 .

[38]  Chun Hung Cheng,et al.  Tight robust interval observers: An LP approach , 2008, 2008 47th IEEE Conference on Decision and Control.

[39]  Christophe Combastel,et al.  A Stable Interval Observer for LTI Systems with No Multiple Poles , 2011 .

[40]  David W. Coit,et al.  System reliability modeling considering the dependence of component environmental influences , 1999, Annual Reliability and Maintainability. Symposium. 1999 Proceedings (Cat. No.99CH36283).

[41]  Yih-Fang Huang,et al.  Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor , 1985, 1985 24th IEEE Conference on Decision and Control.

[42]  V. A. Kopnov Optimal degradation processes control by two-level policies , 1999 .