Models for lifetime estimation: an overview with focus on applications to wind turbines

This paper provides an overview of models and methods for estimation of lifetime of technical components. Although the focus in this paper is on wind turbine applications, the major content of the paper is of general nature. Thus, most of the paper content is also valid for lifetime models applied to other technical systems. The models presented and discussed in this paper are classified in different types of model classes. The main classification used in this paper divides the models in the following classes: physical models, stochastic models, data-driven models and artificial intelligence, and combined models. The paper provides an overview of different models for the different classes. Furthermore, advantages and disadvantages of the models are discussed, and the estimation of model parameters is briefly described. Finally, a number of literature examples are given in this paper, providing an overview of applications of different models on wind turbines.

[1]  Bhaskar D. Rao,et al.  On-line learning algorithms for locally recurrent neural networks , 1999, IEEE Trans. Neural Networks.

[2]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1988, IJCAI 1989.

[3]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[4]  R.P.L. Nijssen,et al.  Fatigue life prediction and strength degradation of wind turbine rotor blade composites , 2006 .

[5]  R. Cooke.  Experts in Uncertainty: Opinion and Subjective Probability in Science , 1991 .

[6]  J. Charles Kerkering,et al.  Eliciting and Analyzing Expert Judgment, A Practical Guide , 2002, Technometrics.

[7]  Paul Fleming,et al.  Use of SCADA Data for Failure Detection in Wind Turbines , 2011 .

[8]  Peter Tavner,et al.  Machine and Converter Reliabilities in Wind Turbines , 2006 .

[9]  Herbert J. Sutherland,et al.  Application of the U.S. high cycle fatigue data base to wind turbine blade lifetime predictions , 1995 .

[10]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[11]  Knut O. Ronold,et al.  Reliability-based fatigue design of wind-turbine rotor blades , 1999 .

[12]  A. Srivastava,et al.  Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks , 2009, Reliab. Eng. Syst. Saf..

[13]  Hassan Zahedi,et al.  Repairable Systems Reliability: Modeling, Inference, Misconceptions and Their Causes , 1989 .

[14]  Peter Tavner,et al.  Improving the reliability of wind turbine generation and its impact on overall distribution network reliability , 2005 .

[15]  W. Nelson.  Statistical Methods for Reliability Data , 1998 .

[16]  Miguel A. Sanz-Bobi,et al.  SIMAP: Intelligent System for Predictive Maintenance: Application to the health condition monitoring of a windturbine gearbox , 2006, Comput. Ind..

[17]  Sheldon M. Ross,et al.  Stochastic Processes , 2018, Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics.

[18]  Necip Doganaksoy.  Practical Reliability Engineering, 4th edition, Patrick D. T. O'Connor, Wiley, 2002, 540 pages , 2005 .

[19]  J. C. Marín,et al.  Study of damage and repair of blades of a 300 kW wind turbine , 2008 .

[20]  Enrico Zio,et al.  A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system , 2010, Reliab. Eng. Syst. Saf..

[21]  Peter Tavner,et al.  Influence of Wind Speed on Wind Turbine Reliability , 2006 .

[22]  Mohammed Kishk,et al.  Modelling System Failures to Optimise Wind Turbine Maintenance , 2007 .

[23]  Bo Henry Lindqvist,et al.  The Trend-Renewal Process for Statistical Analysis of Repairable Systems , 2003, Technometrics.

[24]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[25]  Author ' s personal copy Analyzing bearing faults in wind turbines : A data-mining approach , 2012 .

[26]  Herbert J. Sutherland,et al.  On the Fatigue Analysis of Wind Turbines , 1999 .

[27]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[28]  M. Farid Golnaraghi,et al.  Prognosis of machine health condition using neuro-fuzzy systems , 2004 .

[29]  P. O'Connor,et al.  Practical Reliability Engineering , 1981 .

[30]  Ian D. Watson,et al.  An Introduction to Case-Based Reasoning , 1995, UK Workshop on Case-Based Reasoning.

[31]  Z. Hameed,et al.  State based models applied to offshore wind turbine maintenance and renewal , 2011 .

[32]  Andrew Kusiak,et al.  The prediction and diagnosis of wind turbine faults , 2011 .

[33]  N. Dowling.  Mechanical Behavior of Materials: Engineering Methods for Deformation, Fracture, and Fatigue , 1993 .

[34]  Yu Ding,et al.  Season-Dependent Condition-Based Maintenance for a Wind Turbine Using a Partially Observed Markov Decision Process , 2010, IEEE Transactions on Power Systems.

[35]  John Dalsgaard Sørensen,et al.  On risk-based operation and maintenance of offshore wind turbine components , 2011, Reliab. Eng. Syst. Saf..

[36]  Simon J. Watson,et al.  Physics of Failure approach to wind turbine condition based maintenance , 2009 .

[37]  L. Peel,et al.  Data driven prognostics using a Kalman filter ensemble of neural network models , 2008, 2008 International Conference on Prognostics and Health Management.

[38]  F. Coolen,et al.  On modelling of grouped reliability data for wind turbines , 2010 .

[39]  Noureddine Zerhouni,et al.  Framework for a distributed and hybrid prognostic system , 2007 .

[40]  P. Grambsch.  Survival and Event History Analysis: A Process Point of View by AALEN, O. O., BORGAN, O., and GJESSING, H. K. , 2009 .

[41]  Lina Bertling,et al.  An Approach for Condition-Based Maintenance Optimization Applied to Wind Turbine Blades , 2010, IEEE Transactions on Sustainable Energy.

[42]  Marvin Rausand,et al.  System Reliability Theory: Models, Statistical Methods, and Applications , 2003 .

[43]  Jery R. Stedinger,et al.  Water Resources Systems Planning And Management , 2006 .

[44]  Peter Tavner,et al.  Reliability analysis for wind turbines with incomplete failure data collected from after the date of initial installation , 2009, Reliab. Eng. Syst. Saf..

[45]  Nils J. Nilsson,et al.  Artificial Intelligence: A New Synthesis , 1997 .

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

[47]  Peter Tavner,et al.  Reliability analysis for wind turbines , 2007 .

[48]  F. P. Maciel Barbosa,et al.  Forecast of faults in a wind turbine gearbox , 2012 .

[49]  L Poole David,et al.  Artificial Intelligence: Foundations of Computational Agents , 2010 .

[50]  Peter Tavner,et al.  Reliability of wind turbine subassemblies , 2009 .

[51]  Jannie Sønderkær.  Bayesian Networks as a Decision Tool for O&M of Offshore Wind Turbines , 2015 .

[52]  J. Bert Keats,et al.  Statistical Methods for Reliability Data , 1999 .

[53]  Eric B. Bartlett,et al.  Nuclear power plant status diagnostics using an artificial neural network , 1992 .

[54]  Jay Lee,et al.  A prognostic algorithm for machine performance assessment and its application , 2004 .