Application of physical failure models to enable usage and load based maintenance

The efficiency of a preventive maintenance process largely depends on the ability to predict the replacement intervals of components. Considering the actual usage of the system increases the accuracy of this prediction. The present paper proposes two new maintenance concepts, that combine the benefits of traditional static concepts and condition based maintenance. These new concepts, usage based maintenance and load based maintenance, apply usage or load parameters that are monitored during service to perform a physical model-based assessment of the system condition. The new concepts are positioned within the range of existing maintenance concepts. Also, the role of physical models in maintenance modelling in general is explained and the origin of uncertainty in the predicted service life is discussed. Moreover, it is demonstrated how the monitoring of usage, loads or condition can reduce this uncertainty and increase the service life, by extending existing work in this field. Finally, the different concepts are applied to a gas turbine blade case study to illustrate the benefits of the proposed concepts.

[1]  Tiedo Tinga,et al.  Time-incremental creep–fatigue damage rule for single crystal Ni-base superalloys , 2009 .

[2]  M.J. Roemer,et al.  Prognostic enhancements to diagnostic systems for improved condition-based maintenance [military aircraft] , 2002, Proceedings, IEEE Aerospace Conference.

[3]  P.W. Kalgren,et al.  Application of Prognostic Health Management in Digital Electronic Systems , 2007, 2007 IEEE Aerospace Conference.

[4]  Frank Grooteman,et al.  A stochastic approach to determine lifetimes and inspection schemes for aircraft components , 2008 .

[5]  M. Pecht,et al.  Material failure mechanisms and damage models , 1991 .

[6]  N. Vchare,et al.  Enabling electronic prognostics using thermal data , 2006 .

[7]  Matthieu van der Heijden,et al.  On the availability of a k-out-of-N system given limited spares and repair capacity under a condition based maintenance strategy , 2004, Reliab. Eng. Syst. Saf..

[8]  Marcello Braglia,et al.  Failure rate prediction with artificial neural networks , 2005 .

[9]  Chris P. Tsokos,et al.  Theory and Applications of Reliability , 1977 .

[10]  W. Klingenberg,et al.  Typology of condition based maintenance , 2011 .

[11]  C. Byington,et al.  DYNAMIC MODELING AND WEAR-BASED REMAINING USEFUL LIFE PREDICTION OF HIGH POWER CLUTCH SYSTEMS , 2005 .

[12]  Christophe Bérenguer,et al.  Predictive maintenance policy for a gradually deteriorating system subject to stress , 2009, Reliab. Eng. Syst. Saf..

[13]  M. Marseguerra,et al.  Simulation modelling of repairable multi-component deteriorating systems for 'on condition' maintenance optimisation , 2002, Reliab. Eng. Syst. Saf..

[14]  Mahesh D. Pandey,et al.  Discounted cost model for condition-based maintenance optimization , 2010, Reliab. Eng. Syst. Saf..

[15]  P. L Hall,et al.  Probabilistic physics-of-failure models for component reliabilities using Monte Carlo simulation and Weibull analysis: a parametric study , 2003, Reliab. Eng. Syst. Saf..

[16]  Tiedo Tinga,et al.  Stress intensity factors and crack propagation in a single crystal nickel-based superalloy , 2006 .

[17]  Stephen C. Hora,et al.  Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management , 1996 .

[18]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[19]  Carl S. Byington,et al.  An Overview of Selected Prognostic Technologies With Application to Engine Health Management , 2006 .

[20]  Stephen J. Engel,et al.  Prognostics, the real issues involved with predicting life remaining , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[21]  Gang Niu,et al.  Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance , 2010, Reliab. Eng. Syst. Saf..

[22]  Joseph H. Saleh,et al.  Beyond its cost, the value of maintenance: An analytical framework for capturing its net present value , 2009, Reliab. Eng. Syst. Saf..

[23]  Hongzhou Wang,et al.  A survey of maintenance policies of deteriorating systems , 2002, Eur. J. Oper. Res..

[24]  Rafael Gouriveau.  A fuzzy approach of online reliability modeling and estimation , 2008 .

[25]  R. Orsagh,et al.  Prognostic health management for avionics system power supplies , 2005, 2005 IEEE Aerospace Conference.

[26]  Jeremy Sheldon,et al.  Prognostics/diagnostics for gas turbine engine bearings , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

[27]  Tiedo Tinga,et al.  Incorporating strain gradient effects in a multiscale constitutive framework for nickel-base superalloys , 2008 .

[28]  Antoine Grall,et al.  A condition-based maintenance policy for stochastically deteriorating systems , 2002, Reliab. Eng. Syst. Saf..

[29]  Michael J. Roemer,et al.  A Comprehensive Prognostics Approach for Predicting Gas Turbine Engine Bearing Life , 2004 .

[30]  Charles R Farrar,et al.  Damage prognosis: the future of structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[31]  Haritha Saranga.  Relevant condition‐parameter strategy for an effective condition‐based maintenance , 2002 .

[32]  Mitra Fouladirad,et al.  On the use of on-line detection for maintenance of gradually deteriorating systems , 2008, Reliab. Eng. Syst. Saf..

[33]  Enrico Zio,et al.  Optimizing maintenance and repair policies via a combination of genetic algorithms and Monte Carlo simulation , 2000, Reliab. Eng. Syst. Saf..

[34]  E.R. Brown,et al.  Prognostics and Health Management A Data-Driven Approach to Supporting the F-35 Lightning II , 2007, 2007 IEEE Aerospace Conference.

[35]  K. Goebel,et al.  Standardizing research methods for prognostics , 2008, 2008 International Conference on Prognostics and Health Management.

[36]  Donald Barker,et al.  Simplified terrain identification and component fatigue damage estimation model for use in a health and usage monitoring system , 2007, Microelectron. Reliab..

[37]  Rommert Dekker,et al.  Modelling and Optimizing Imperfect Maintenance of Coatings on Steel Structures , 2007 .

[38]  P. A. Engel,et al.  Failure models for mechanical wear modes and mechanisms , 1993 .

[39]  Enrico Zio,et al.  Reliability engineering: Old problems and new challenges , 2009, Reliab. Eng. Syst. Saf..

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

[41]  K. F. Fraser.  An Overview of Health and Usage Monitoring Systems (HUMS) for Military Helicopters , 1994 .

[42]  A. Hess,et al.  Challenges, issues, and lessons learned chasing the "Big P". Real predictive prognostics. Part 1 , 2005, 2005 IEEE Aerospace Conference.

[43]  Michael Pecht,et al.  Environment and Usage Monitoringof Electronic Products for Health Assessment and Product Design , 2007 .

[44]  S. R. Hunt,et al.  Validation of the Eurofighter Typhoon structural health and usage monitoring system , 2001 .

[45]  Jon C. Helton,et al.  Guest editorial: treatment of aleatory and epistemic uncertainty in performance assessments for complex systems , 1996 .

[46]  Erhan Çinlar,et al.  SHOCK AND WEAR MODELS AND MARKOV ADDITIVE PROCESSES , 1977 .

[47]  Nozer D. Singpurwalla,et al.  Survival in Dynamic Environments , 1995 .

[48]  A. Hess,et al.  Challenges, Issues, and Lessons Learned Chasing the “ Big P”: Real Predictive Prognostics Part 2 , 2006, 2006 IEEE Aerospace Conference.