A Fuzzy-FMEA Risk Assessment Approach for Offshore Wind Turbines
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Abstract
Failure Mode and Effects Analysis (FMEA) has been extensively used by wind turbine assembly manufacturers for risk and reliability analysis. However, several limitations are associated with its implementation in offshore wind farms: (i) the failure data gathered from SCADA system is often missing or unreliable, and hence, the assessment information of the three risk factors (i.e., severity, occurrence, and fault detection) are mainly based on experts’ knowledge; (ii) it is rather difficult for experts to precisely evaluate the risk factors; (iii) the relative importance among the risk factors is not taken into consideration, and hence, the results may not necessarily represent the true risk priorities; and etc. To overcome these drawbacks and improve the effectiveness of the traditional FMEA, we develop a fuzzy-FMEA approach for risk and failure mode analysis in offshore wind turbine systems. The information obtained from the experts is expressed using fuzzy linguistics terms, and a grey theory analysis is proposed to incorporate the relative importance of the risk factors into the determination of risk priority of failure modes. The proposed approach is applied to an offshore wind turbine system with sixteen mechanical, electrical and auxiliary assemblies, and the results are compared with the traditional FMEA.
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Offshore wind turbine, Failure Modes and Effect Analysis (FMEA), Fuzzy rule base, Risk assessment, Grey theory
Andrews, J.D. and Moss, T.R. (1993) Reliability and risk assessment, Longmans.
Arabian-Hoseynabadi, H., Oraee, H. and Tavner, P.J. (2010), Failure modes and effects analysis (FMEA) for wind turbines. International Journal of Electrical Power & Energy Systems 32(7), 817–824.
Bilgili M., Yasar, A. and Simsek, E. (2011) Offshore wind power development in Europe and its comparison with onshore counterpart, Renewable and Sustainable Energy Reviews 15, 905–915.
Braglia, M. (2000) MAFMA: multi-attribute failure mode analysis. International Journal of Quality and Reliability Management 17(9), 1017–1033.
Carlin, P.W., Laxson, A.S. and Muljadi, E.B. (2003) The history and state of the art of variable-speed wind turbine technology. Wind Energy 6(2), 129–159.
Carlson, R., Voltolini, H., Runcos, F. and Kuo-Peng, P. (2006) A performance comparison between brush and brushless doubly fed asynchronous generators for wind power systems, in Proceedings of the International Conference on Renewable Energies and Power Quality, Balearic Island, April 5–7, Spain.
Chang, C.-L., Wei, C.-C. and Lee, Y.-H. (1999) Failure mode and effects analysis using fuzzy method and grey theory. Kybernetes 28(9), 1072–1080.
Chen, C.B. and Klien, C.M. (1997) A simple approach to ranking a group of aggregated fuzzy utilities. IEEE Transactions on Systems Man and Cybernetics, Part B, 27(1), 26–35.
Cheng, C.-H. (1998) A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets and Systems 95(3), 307–317.
Chin, K. S., Chan, A. and Yang, J.B. (2008) Development of a fuzzy FMEA based product design system. International Journal of Advanced Manufacturing Technology 36, 633–649.
Deng, J. (1989). Introduction to grey system theory. Journal of Grey Systems 1(1), 1–24.
Dinmohammadi, F. and Shafiee, M. (2013) An economical FMEA-based risk assessment approach for wind turbine systems. European Safety, Reliability and Risk Management (ESREL), 30 Sep.–2 Oct., Amsterdam, Netherland.
European Wind Energy Association, Wind in power, 2011 European statistics, published in Feb. 2012.
Gargama, H. and Chaturvedi, S.K (2011) Criticality assessment models for failure mode effects and criticality analysis using fuzzy logic. IEEE Transactions on Reliability, 60(1), 102–110.
Gilchrist, W. (1993). Modeling failure mode and effect analysis. International Journal of Quality & Reliability Management 10(5), 16–23.
Guimarães, A.C.F. and Lapa, C.M.F. (2007) Fuzzy inference to risk assessment on nuclear engineering systems. Applied Soft Computing 7(1), 17–28.
Kahrobaee, S. and Asgarpoor, S. (2011) Risk-based Failure Mode and Effect Analysis for wind turbines (RB-FMEA) North American Power Symposium (NAPS), August 4–6, pp 1–7, Boston, USA.
Keskin, G.-A. and Özkan, C. (2009) An alternative evaluation of FMEA: Fuzzy ART algorithm. Quality and Reliability Engineering International, 25, 647–661.
Kutlu, A.C. and Ekmekçioğlu, M. (2012) Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems with Applications 39, 61–67.
Li, H. and Chen, Z. (2008) Overview of different wind generator systems and their comparisons. IET Renewable Power Generation 2(2), 123-138.
Liu, H.-C., Liu, L., Bian, Q.-H., Lin, Q.-L., Dong, N. and Xu, P.-C. (2011) Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory. Expert Systems with Applications 38, 4403–4415.
Márquez, F.P.G, Tobias, A.M., Pérez, J.M.P. and Papaelias, M. (2012) Condition monitoring of wind turbines: Techniques and methods. Renewable Energy 46, 169–178.
Muller, S., Deicke, M. and De Doncker, R.W. (2002) Doubly fed induction generator systems for wind turbines. IEEE Industry Applications Magazine 8(3), 26–33.
Pillay, A. and Wang, J. (2003) Modified failure mode and effects analysis using approximate reasoning. Reliability Engineering and System Safety 79, 69–85.
Shafiee, M., Patriksson, M. and Strömberg, A.-B. (2013) An optimal number-dependent preventive maintenance strategy for offshore wind turbine blades considering logistics. Advances in Operations Research (In Print).
Sharma, R.K., Kumar, D. and Kumar, P. (2005) Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling. International Journal of Quality & Reliability Management 22(9), 986–1004.
Tavner, P.J., Xiang, J. and Spinato, F. (2007) Reliability analysis for wind turbines. Wind Energy 10(1), 1–18.
Yang, Z., Bonsall, S., and Wang, J. (2008) Fuzzy rule-based bayesian reasoning approach for prioritization of failures in FMEA. IEEE Transactions on Reliability, 57(3), 517–528.