Ensemble of Kernel Regression Models for Assessing the Health State of Choke Valves in Offshore Oil Platforms

References

• Garvey, D.R., Baumann, J., Lehr, J., Hughes, B., and Hines, J.W. 2009. Pattern Recognition Based Remaining Useful Life Estimation of Bottom Hole Assembly Tools. SPE/IADC Drilling Conference and Exhibition, March 17-19, Amsterdam, NL. doi: 10.2118/118769-MS.
   Google Scholar
• Vachtsevanos, G., Lewis, F.L., Roemer, M., Hess, A., and Wu, B. 2006. Intelligent Fault Diagnosis and Prognosis for Engineering Systems, 1st edition, Hoboken, New Jersey, John Wiley & Sons.
   Google Scholar
• Jarrell, D.B., Sisk, D.R., and Bond, L.J. 2004. Prognostics and Condition-Based Maintenance: A New Approach to Precursive Metrics, Nuclear Technology, Vol. 145, pp. 275–286.
   Web of Science ®Google Scholar
• Hines, J.W., and Usynin, A. 2008. Current Computational Trends in Equipment Prognostics. International Journal of Computational Intelligence Systems, Vol. 1, Issue 1, pp. 94–102. doi: 10.2991/ijcis.2008.1.1.7.
   Web of Science ®Google Scholar
• Nystad, B.H., Gola, G., Hulsund, J.E., and Roverso, D. 2010. Technical Condition Assessment and Remaining Useful Life Estimation of Choke Valves subject to Erosion. Proc. PHM Society 2010 Ann. Conf., October 11-13, Portland, OR.
   Google Scholar
• Haugen, K., Kvernvold, O., Ronold, A., Sandberg, R. 1995. Sand Erosion of Wear Resistant Materials: Erosion in Choke Valves. Wear, Vol. 186-187, Part 1, pp. 179–188. doi:10.1016/0043-1648(95)07158-X.
   Google Scholar
• Dunn, J.C. 1974. A fuzzy relative of the isodata process and its use in detecting compact, well separated clusters. Cybernetics and Systems, Vol 3, Issue 3, pp. 32–57.
   Google Scholar
• Jain, A.K., Murty, M.N., and Flyn, P.J. 1999. Data Clustering: A Review. ACM Computing Surveys (CSUR), Vol. 31, Issue 3, pp. 264–323. doi: 10.1145/331499.331504.
   Web of Science ®Google Scholar
• Zio, E., and Baraldi, P. 2005. Identification of nuclear transients via optimized fuzzy clustering. Ann. of Nucl. Energy 32, pp.1068–1080. doi: 10.1016/j.anucene.2005.02.012.
   Google Scholar
• Nadaraya, E.A. 1964. On Estimating Regression. Theory of Probability and Its Applications, Vol. 10, pp. 186–190. doi: 10.1137/1109020.
   Web of Science ®Google Scholar
• Atkeson, C.G., Moore, A.W., and Schaal, S. 1997. Locally Weighted Learning. Artificial Intelligence Review, Vol. 11, pp. 11–73. doi: 10.1.1.73.9932.
   Web of Science ®Google Scholar
• Perrone, M.P., and Cooper, L.N. 1992. When networks disagree: ensemble methods for hybrid neural networks, National Science Fundation, USA. doi: 10.1.1.32.3857.
   Google Scholar
• Bonissone, P.P., 2010. Soft Computing: A Continuously Evolving Concept, International Journal of Computational Intelligence Systems, Vol.3, No. 2, pp. 237–24.
   Web of Science ®Google Scholar
• Saaty, T.L. 1980. The analytic Hierarchy Process, Planning, Priority Setting, Resource Allocation. McGraw-Hill, New York.
   Google Scholar
• Wold, K., Hopkins, S., Jakobsen, T., Lilleland, S.E., Roxar, R.S., and Brandal, Ø. 2010. New Generation Software Integrates Intrusive and Non-intrusive Systems for Corrosion and Sand/erosion Monitoring. SPE Int. Conf. on Oilfield Corrosion, May 24-25, Aberdeen, UK. doi: 10.2118/130569-MS.
   Google Scholar
• Nøkleberg, L., and Søntvedt, T. 1995. Erosion in choke valves-oil and gas industry applications. Wear, Vol. 186- 187, Part 2, pp. 401–412. doi: 10.1016/0043-1648(95)07138-5.
   Google Scholar
• Birchenough, P.M., Cornally, D., Dawson, S.G.B., McCarthy, P., and Susden, S. 1994. Assessment of Choke Valve Erosion in a High-Pressure, High-Temperature Gas Condensate Well Using TLA. European Petroleum Conference, October 25-27, London, UK. doi: 10.2118/28887-MS.
   Google Scholar
• Metso Automation. 2005. Flow Control Manual. 4th edition.
   Google Scholar
• Myers, J.L., Well, A.D., and Lorch, R.F. 2010. Research Design and Statistical Analysis. Taylor & Francis, New York.
   Google Scholar
• Devijver, P.A., and Kittler, J. 1982. Pattern Recognition: A Statistical Approach. Prentice/Hall, Englewood Cliffs, NJ.
   Google Scholar
• Yuan, B., Klir, G., Swan-Stone, J. 1995. Evolutionary fuzzy c-means clustering algorithm. Proc. Fourth IEEE Int. Conf. Fuzzy Syst., Vol 4, pp. 2221–2226. doi: 10.1109/FUZZY.2007.4295668.
   Google Scholar
• Zio, E., Baraldi, P., 2004. A fuzzy clustering approach for transients classification. In: Ruan, D., D_hondt, P., Cock, M.D., Nachtegael, M., Kerre, E.E. (Eds.), Applied Computational Intelligence. Word Scientific. doi: 10.1142/9789812702661_0103.
   Google Scholar
• Baraldi, P., Zio, E., Gola, G., Roverso, D., and Hoffmann, M. 2009. A procedure for the reconstruction of faulty signals by means of an ensemble of regression models based on principal components analysis, NPICHMIT Topical Meeting, April 5-9, Knoxville, USA.
   Google Scholar
• Fantoni, P.F., and Mazzola, A. 1996. Multiple-Failure Signal Validation in Nuclear Power Plants using Artificial Neural Networks, Nuclear technology, Vol. 113, Issue 3, pp. 368–374.
   Web of Science ®Google Scholar
• Marseguerra, M., Zio, E., and Marcucci, F. 2006. Continuous Monitoring and Calibration of UTSG Process Sensors by Autoassociative Artificial Neural Network. Nuclear Technology, Vol. 154, Issue 2, pp. 224–236.
   Web of Science ®Google Scholar
• Sun, B.Y., Huang, D.S., and Fang, H.T. 2005. Lidar Signal Denoising Using Least-Squares Support Vector Machine. IEEE Signal Processing Letters, Vol. 12, Issue 2, pp.101–104. doi: 10.1109/LSP.2004.836938(410)12.
   Web of Science ®Google Scholar
• Chevalier, R., Provost, D., and Seraoui, R. 2009. Assessment of Statistical and Classification Models For Monitoring EDF's Assets, Sixth American Nuclear Society Int. Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, Knoxville, USA, 2009.
   Google Scholar
• Wand, M.P., and Schucany, W.R. 1990. Gaussian-based kernels for curve estimation and window width selection. Canadian Journal of Statistics, Vol. 18(3), pp. 197–204.
   Web of Science ®Google Scholar
• Polikar, R. 2007. Bootstrap-inspired techniques in computational intelligence, IEEE Signal Processing Magazine, Vol. 59, pp. 59–72. doi: 10.1109/MSP.2007.4286565.
   Google Scholar
• Baraldi, P., Cammi, A., Mangili, F., and Zio, E. 2010. Local Fusion of an Ensemble of Models for the Reconstruction of Faulty Signals, IEEE Trans. on Nucl. Sci., Vol.57, Issue 2, pp. 793–806. doi: 10.1109/TNS.2010.2042968.
   Google Scholar
• Hruschka, E., Campello, S., Freitas, A. De Carvalho, A. IEEE Transactions on A Survey of Evolutionary Algorithms for Clustering, Systems, Man, and Cybernetics, Part C: Applications and Reviews,
   Google Scholar
• J. A. Hartigan and M. A. Wong, “A k-means clustering algorithm,” Applied Statistics, Vol. 28, pp.100–108, 1979.
   Google Scholar