Remaining useful life assessment via residual generator approach — A SOH virtual sensor concept

Based on analytical redundancy, specific set of residuals are designed to be sensitive to each component fault. The residual set may then represent as a measure of the loss of performance of the component in the system and thus, reflect the state of health of the component. The dynamic evolution of the residual can be considered as an image of the evolution of the component health, and can be modeled as the response of the component to the stress factors and the natural aging. The residual represents a degradation virtual sensor. Based on this prediction related to the various stress factors, the range of possible values of the remaining useful life (RUL) of the component can be predicted. The main contribution of the proposed paper is to present a new approach for RUL estimation based on a State Of Health degradation virtual sensor design. The range of values of the RUL can supply downstream applications for decision aids in maintenance or control. For illustration, the proposed approach was applied to the RUL prediction of actuators in a function of stress factors.

[1]  Jan M. van Noortwijk,et al.  A survey of the application of gamma processes in maintenance , 2009, Reliab. Eng. Syst. Saf..

[2]  John E. White,et al.  Detection Filter Design: Spectral Theory and Algorithms , 1986, 1986 American Control Conference.

[3]  Simona Onori,et al.  A prognostic methodology for interconnected systems: preliminary results , 2012 .

[4]  Kai Goebel,et al.  A Survey of Artificial Intelligence for Prognostics , 2007, AAAI Fall Symposium: Artificial Intelligence for Prognostics.

[5]  Benoît Iung,et al.  Choquet integral parameters inference for health indicators fusion within multi-levels industrial systems: application to components in series , 2012 .

[6]  Jennie Si,et al.  Fault isolation filter design for linear systems , 1994, AIAA/IEEE Digital Avionics Systems Conference. 13th DASC.

[7]  B. Iung,et al.  Bottom-up capacities inference for health indicator fusion within multi-level industrial systems , 2012, 2012 IEEE Conference on Prognostics and Health Management.

[8]  Satish Nagarajaiah,et al.  Observer‐based structural damage detection using genetic algorithm , 2013 .

[9]  Christophe Bérenguer,et al.  Condition‐Based Maintenance with Imperfect Preventive Repairs for a Deteriorating Production System , 2012, Qual. Reliab. Eng. Int..

[10]  Zheng Wang,et al.  Reliability Degradation of Mechanical Components and Systems , 2008 .

[11]  G. Rizzoni,et al.  A new interpretation of the fault detection filter Part 1: Closed-form algorithm , 1994 .

[12]  M. Massoumnia.  A geometric approach to the synthesis of failure detection filters , 1986 .

[13]  Douglas E. Adams,et al.  A nonlinear dynamical systems framework for structural diagnosis and prognosis , 2002 .

[14]  Harold Lee Jones,et al.  Failure detection in linear systems , 1973 .

[15]  J.-Y. Keller.  Fault isolation filter design for linear stochastic systems , 1999, Autom..

[16]  D. Karunkuzhali,et al.  AI Technique in Diagnostics and Prognostics , 2013 .