全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

Gas Turbine Blade Damper Optimization Methodology

МЕТОДОЛОГ Я СТРАТЕГ ЕКСПЛУАТАЦ АВ АЦ ЙНИХ ГАЗОТУРБ ННИХ ДВИГУН В ЗА ТЕХН ЧНИМ СТАНОМ З КОНТРОЛЕМ Р ВНЯ ЛЬОТНО ПРИДАТНОСТ Methodology strategy manual aviation gas turbine engines for the technical condition controls the level of airworthiness Методология стратегии эксплуатации авиационных газот

Gas Chromatographic Methodology for the Determination of Some Halogenated Pesticides

Gas turbine diagnostic system

ВИЗНАЧЕННЯ В БРАЦ ЙНИХ ХАРАКТЕРИСТИК ЛОПАТКИ ГАЗОТУРБ ННИХ ДВИГУН В БЕЗКОНТАКТНИМ МЕТОДОМ Determination of vibration characteristics of blades of gas turbine engines contactless method Определение вибрационных характеристик лопатки газотурбинных двигателей бесконтактным методом

Gas turbine engine turbine blade damaging estimate in maintenance Оценка повреждаемости лопаток турбин газотурбинных двигателей в эксплуатации ОЦ НКА ПОШКОДЖУВАНОСТ ЛОПАТОК ТУРБ Н ГАЗОТУРБ ННИХ ДВИГУН В В ЕКСПЛУАТАЦ

Exergy efficiency of domestic and foreign gas-turbine installations

Estimation method of margin gasdynamic stability of the gas turbine engine while in service Способ оценки запаса газодинамической устойчивости компрессора газотурбинного двигателя в процессе эксплуатации СПОС Б ОЦ НКИ ЗАПАСУ ГАЗОДИНАМ ЧНО СТ ЙКОСТ КОМПРЕСОРА ГАЗОТУРБ ННОГО ДВИГУНА В ПРОЦЕС ЕКСПЛУАТАЦ

New model of work of the gas-turbine engine Новая модель безопасной работы газотурбинного двигателя НОВА МОДЕЛЬ БЕЗПЕЧНО РОБОТИ ГАЗОТУРБ ННОГО ДВИГУНА

Micro Gas Turbine – A Review

更多...

Gas Turbine Health State Determination: Methodology Approach and Field Application

DOI: 10.1155/2012/142173

Full-Text   Cite this paper   Add to My Lib

Abstract:

A reduction of gas turbine maintenance costs, together with the increase in machine availability and the reduction of management costs, is usually expected when gas turbine preventive maintenance is performed in parallel to on-condition maintenance. However, on-condition maintenance requires up-to-date knowledge of the machine health state. The gas turbine health state can be determined by means of Gas Path Analysis (GPA) techniques, which allow the calculation of machine health state indices, starting from measurements taken on the machine. Since the GPA technique makes use of field measurements, the reliability of the diagnostic process also depends on measurement reliability. In this paper, a comprehensive approach for both the measurement validation and health state determination of gas turbines is discussed, and its application to a 5?MW gas turbine working in a natural gas compression plant is presented. 1. Introduction Maintaining high levels of availability and reliability is an essential objective for all production units, especially for those that are subject to high costs due to loss of production. Nonscheduled stops due to unforeseen faults cause relevant costs related to the reduction or the interruption of the process and to the consequent repairing actions. For this reason, in strategic applications, stand-by machines are usually required to ensure the desired level of availability. In the last decades, gas turbines have been more and more used either for power generation or as mechanical drive (e.g., in natural gas compression plants), thanks to their favorable characteristics with respect to other technologies, such as low emissions and high availability and reliability. In particular, the latter issues represent winning features of gas-turbine-based power plants. Hence, in order to utilize these systems as effectively as possible, the management of machine maintenance must be optimized. The optimization of maintenance management, which should lead to cost saving and increase in machine availability, can be performed by supporting gas turbine preventive maintenance (which comes from manufacturer experience in terms of component life and performance degradation versus working hours and is performed according to a priori schedules, regardless of the effective gas turbine health state) with on-condition maintenance, which consists of ?“ad hoc” actions descending from gas turbine actual operating state [1–7]. Therefore, On-condition maintenance requires up-to-date knowledge of the machine health state in real time. One of the most

References

[1]  R. F. Hoeft, “Heavy duty gas turbine operating and maintenance considerations,” in Proceedings of the 39th GE Turbine State-of-the-Art Technology Seminar, GER-3620D, 1996.
[2]  T. P. Schmitt and C. G. Petroff, “Gas turbine performance monitoring and testing,” in Proceedings of the 39th GE Turbine State-of-the-Art Technology Seminar, GER-3958, 1996.
[3]  R. Bettocchi, M. Pinelli, P. R. Spina, M. Venturini, and S. Sebastianelli, “A system for health state determination of natural gas compression gas turbines,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper 2001-GT-223, 2001.
[4]  D. Therkorn, “Remote monitoring & diagnostic for combined-cycle power plants,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2005-68710, pp. 697–703, June 2005.
[5]  H. R. Depold and J. Siegel, “Using diagnostics and prognostics to minimize the cost of ownership of gas turbines,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2006-91183, pp. 845–851, May 2006.
[6]  E. Hindle, R. Van Stone, C. Brogan, J. Vandike, K. Dale, and N. Gibson, “A prognostic and diagnostic approach to engine health management,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2006-90614, pp. 673–680, May 2006.
[7]  L. C. Jaw, “Recent advancements in aircraft Engine Health Management (EHM) technologies and recommendations for the next step,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2005-68625, pp. 683–695, June 2005.
[8]  L. A. Urban, “Gas path analysis applied to turbine engine condition monitoring,” in Proceedings of the AIAA/SAE 8th Joint Propulsion Conference, New Orleans, La, USA, 1972.
[9]  A. Stamatis, K. Mathioudakis, and K. D. Papailiou, “Adaptive simulation of gas turbine performance,” ASME Journal of Engineering for Gas Turbines and Power, vol. 112, pp. 168–175, 1990.
[10]  E. Benvenuti, R. Bettocchi, G. Cantore, G. Negri di Montenegro, and P. R. Spina, “Gas turbine cycle modeling oriented to component performance evaluation from limited design or test data,” in Proceedings of the 7th ASME COGEN-TURBO, vol. 8, pp. 327–337, IGTI, Bournemouth, UK, 1993.
[11]  E. Benvenuti, R. Bettocchi, G. Cantore, G. Negri di Montenegro, and P. R. Spina, “Experimental validation of a gas turbine cycle model based on a simultaneous solution method,” in Proceedings of the 8th ASME COGEN-TURBO, vol. 9, pp. 245–255, IGTI, Portland, Ore, USA, 1994.
[12]  R. Bettocchi and P. R. Spina, “Diagnosis of gas turbine operating conditions by means of the inverse cycle calculation,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper 99-GT-185, 1999.
[13]  R. Bettocchi, M. Pinelli, P. R. Spina, and M. Venturini, “Artificial intelligence for the diagnostics of gas turbines—part 1: neural network approach,” Journal of Engineering for Gas Turbines and Power, vol. 129, no. 3, pp. 711–719, 2007.
[14]  R. Bettocchi, M. Pinelli, P. R. Spina, and M. Venturini, “Artificial intelligence for the diagnostics of gas turbines—part II: neuro-fuzzy approach,” Journal of Engineering for Gas Turbines and Power, vol. 129, no. 3, pp. 720–729, 2007.
[15]  R. Bettocchi, M. Pinelli, P. R. Spina, M. Venturini, and G. A. Zanetta, “Assessment of the robustness of gas turbine diagnostics tools based on neural networks,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2006-90118, pp. 603–613, May 2006.
[16]  M. Bianchi, A. Peretto, and P. R. Spina, “Modular dynamic model of multi-shaft gas turbine and validation test,” in Proceedings of the Winter Annual Meeting of ASME, vol. 38, pp. 73–81, AES, Anaheim, Calif, USA, 1998.
[17]  M. Venturini, “Development and experimental validation of a compressor dynamic model,” Journal of Turbomachinery, vol. 127, no. 3, pp. 599–608, 2005.
[18]  M. Morini, M. Pinelli, and M. Venturini, “Development of a one-dimensional modular dynamic model for the simulation of surge in compression systems,” Journal of Turbomachinery, vol. 129, no. 3, pp. 437–447, 2007.
[19]  M. Morini, M. Pinelli, and M. Venturini, “Application of a one-dimensional modular dynamic model for compressor surge avoidance,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2007-27041, pp. 1425–1434, May 2007.
[20]  M. Morini, G. Cataldi, M. Pinelli, and M. Venturini, “A model for the simulation of large-size single-shaft gas turbine start-up based on operating data fitting,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2007-27373, pp. 1849–1856, May 2007.
[21]  M. Morini, M. Pinelli, and M. Venturini, “Analysis of biogas compression system dynamics,” Applied Energy, vol. 86, no. 11, pp. 2466–2475, 2009.
[22]  M. Venturini, “Simulation of compressor transient behavior through recurrent neural network models,” Journal of Turbomachinery, vol. 128, no. 3, pp. 444–454, 2006.
[23]  M. Venturini, “Optimization of a real-time simulator based on recurrent neural networks for compressor transient behavior prediction,” Journal of Turbomachinery, vol. 129, no. 3, pp. 468–478, 2007.
[24]  M. Pinelli, P. R. Spina, and M. Venturini, “Optimized operating point selection for gas turbine health state analysis by using a multi-point technique,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2003-38191, pp. 43–51, June 2003.
[25]  Y. G. Li, “Gas turbine diagnosis using a fault isolation enhanced GPA,” in Proceedings of the Turbine Technical Conference and Exposition, pp. 361–369, June 2004.
[26]  A. I. Zwebek and P. Pilidis, “Application of GPA to combined cycle gas turbine plants,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2004-53026, pp. 225–232, June 2004.
[27]  O. Córdoba, “Gas path analysis study for overhaul engines,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2005-68137, pp. 497–505, June 2005.
[28]  M. Morini, M. Pinelli, P. R. Spina, and M. Venturini, “Influence of blade deterioration on compressor and turbine performance,” Journal of Engineering for Gas Turbines and Power, vol. 132, no. 3, Article ID 032401, 2010.
[29]  P. R. Spina, “Gas turbine performance prediction by using generalized performance curves of compressor and turbine stages,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT-2002-30275, pp. 1073–1082, June 2002.
[30]  M. Bagnoli, M. Bianchi, F. Melino, and P. R. Spina, “Development and validation of a computational code for wet compression simulation of gas turbines,” Journal of Engineering for Gas Turbines and Power, vol. 130, no. 1, Article ID 012004, 2008.
[31]  M. Bagnoli, M. Bianchi, F. Melino et al., “Application of a computational code to simulate interstage injection effects on GE frame 7EA gas turbine,” Journal of Engineering for Gas Turbines and Power, vol. 130, no. 1, Article ID 012001, 2008.
[32]  M. Morini, M. Pinelli, P. R. Spina, and M. Venturini, “Computational fluid dynamics simulation of fouling on axial compressor stages,” Journal of Engineering for Gas Turbines and Power, vol. 132, no. 7, Article ID 072401, 2010.
[33]  M. Morini, M. Pinelli, P. R. Spina, and M. Venturini, “Numerical analysis of the effects of non-uniform surface roughness on compressor stage performance,” Journal of Engineering for Gas Turbines and Power, vol. 133, no. 7, Article ID 072402, 2011.
[34]  M. Pinelli, M. Morini, P. R. Spina, M. Venturini, and C. Ferrari, “Analysis of the effects of simulated fouling on an axial compressor stage through CFD modeling,” in Proceedings of the 9th European Conference on Turbomachinery, M. Sen, G. Bois, M. Manna, and T. Arts, Eds., vol. 1, pp. 261–270, Istanbul, Turkey, 2011.
[35]  F. Melino, M. Morini, A. Peretto, M. Pinelli, and P. R. Spina, “Compressor fouling modeling: relationship between computational roughness and gas turbine operation time,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2011-46089, 2011.
[36]  R. Bettocchi, M. Pinelli, and P. R. Spina, “A multistage compressor test facility: uncertainty analysis and preliminary test results,” Journal of Engineering for Gas Turbines and Power, vol. 127, no. 1, pp. 170–177, 2005.
[37]  M. Morini, M. Pinelli, and M. Venturini, “Acoustic and vibrational analyses on a multi-stage compressor for unstable behavior precursor identification,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2007-27040, pp. 1415–1423, May 2007.
[38]  R. Bettocchi, M. Morini, M. Pinelli, P. R. Spina, M. Venturini, and G. Torsello, “Setup of an experimental facility for the investigation of wet compression on a multistage compressor,” Journal of Engineering for Gas Turbines and Power, vol. 133, no. 10, Article ID 102001, 2011.
[39]  R. Bettocchi, P. R. Spina, and E. Benvenuti, “Set-up of an adaptive method for the diagnosis of gas turbine operating state by using test-Bench measurements,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper 2000-GT-0309, 2000.
[40]  A. Stamatis, K. Mathioudakis, and K. Papailiou, “Optimal measurement and health index selection for gas turbine performance status and fault diagnosis,” Journal of Engineering for Gas Turbines and Power, vol. 114, no. 2, pp. 209–216, 1992.
[41]  M. Pinelli and P. R. Spina, “Gas turbine field performance determination: sources of uncertainties,” Journal of Engineering for Gas Turbines and Power, vol. 124, no. 1, pp. 155–160, 2002.
[42]  Y. G. Li, P. Pilidis, and M. A. Newby, “An adaptation approach for gas turbine design-point performance simulation,” in Proceedings of the Turbine Technical Conference and Exposition, pp. 95–105, usa, June 2005.
[43]  M. Pinelli and M. Venturini, “Improvement of the accuracy in gas turbine health determination,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper 2001-GT-476, 2001.
[44]  M. Pinelli, M. Venturini, and M. Burgio, “Statistical methodologies for reliability assessment of gas turbine measurements,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2003-38407, pp. 787–793, June 2003.
[45]  H. DePold, A. Volponi, J. Siegel, and J. Hull, “Validation of diagnostic data with statistical analysis and embedded knowledge,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper 2003-GT-38764, pp. 573–579, June 2003.
[46]  P. C. Chen and H. Andersen, “The implementation of the data validation process in a gas turbine performance monitoring system,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2005-68429, pp. 609–616, June 2005.
[47]  P. Hartner, J. Petek, P. Pechtl, and P. Hamilton, “Model-based data reconciliation to improve accuracy and reliability of performance evaluation of thermal power plants,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2005-68937, pp. 195–200, June 2005.
[48]  S. C. Gulen and R. W. Smith, “A simple mathematical approach to data reconciliation in a single-shaft combined-cycle system,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2006-90145, pp. 479–491, May 2006.
[49]  K. Mathioudakis and P. Kamboukos, “Assessment of the effectiveness of gas path diagnostic schemes,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2004-53862, pp. 723–731, June 2004.
[50]  S. W. Butler, K. R. Pattipati, A. Volponi, J. Hull, R. Rajamani, and J. Siegel, “An assessment methodology for data-driven and model-based techniques for engine health monitoring,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2006-91096, pp. 823–831, May 2006.
[51]  C. Romessis, P. Kamboukos, and K. Mathioudakis, “The use of probabilistic reasoning to improve least squares based gas path diagnostics,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2006-90619, pp. 681–689, May 2006.
[52]  R. Bettocchi, M. Pinelli, P. R. Spina, and M. Venturini, “Statistical analyses to improve gas turbine diagnostics reliability,” in Proceedings of the IGTC, Paper IGTC2003-Tokyo TS-004, 2003.
[53]  M. Pinelli and M. Venturini, “Operating state historical data analysis to support gas turbine malfunction detection,” in Proceedings of the Turbine Technical Conference and Exposition, ASME IMECE2001/AES-23665, pp. 457–462, November 2001.
[54]  P. R. Spina, “Reliability in the determination of gas turbine operating state,” in Proceedings of the 39th IEEE Conference on Decision and Control, Paper CDC00-INV5805, Sydney, Australia, 2000.
[55]  R. Bettocchi, M. Pinelli, M. Venturini, P. R. Spina, S. Bellagamba, and G. Tirone, “Procedura di calibrazione del programma per la diagnosi funzionale dei turbogas della centrale a ciclo combinato di la spezia,” in Proceedings of the Atti del 57th Congresso Nazionale ATI, Pisa, Italy, 2002.
[56]  A. Gulati, M. Zedda, and R. Singh, “Gas turbine engine and sensor multiple operating point analysis using optimization techniques,” in Proceedings of the 36th AIAA/ASME/ SAE/ASEE Joint Propulsion Conference and Exhibit, AIAA 2000-3716, 2000.
[57]  R. Bettocchi, M. Pinelli, M. Venturini, P. R. Spina, S. Sebastianelli, and F. Bezzi, “A software tool for gas turbine on-condition monitoring and diagnostics,” in Proceedings of OMC The Mediterranean: New Oil and Gas Routes for an Ancient Sea, Ravenna, Italy, 2003.
[58]  P. R. Spina, G. Torella, and M. Venturini, “The use of expert systems for gas turbine diagnostics and maintenance,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT-2002-30033, pp. 127–134, June 2002.
[59]  N. Puggina and M. Venturini, “Development of a statistical methodology for gas turbine prognostics,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2011-45708, 2011.
[60]  A. Cavarzere and M. Venturini, “Application of forecasting methodologies to predict gas turbine behavior over time,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2011-45708, 2011.
[61]  R. Bettocchi, P. R. Spina, M. Pinelli, M. Venturini, and M. Burgio, “Set up of a robust neural network for gas turbine simulation,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT2004-53421, pp. 543–551, June 2004.
[62]  P. R. Spina and M. Venturini, “Gas turbine modeling by using neural networks trained on field operating data,” in Proceedings of the ECOS, vol. 1, pp. 213–222, Padova, Italy, 2007.
[63]  Visual Numerics, Inc., “IMSL MATH/LIBRARY: FORTRAN Subroutines for Mathematical Applications,” Houston, Texas, USA, 1994.
[64]  R. Bettocchi, P. R. Spina, and S. Alliney, “Resolution method for gas turbine mathematical models,” in Proceedings of the 8th ASME COGEN-TURBO, vol. 9, pp. 361–369, Portland, Ore, USA, 1994.
[65]  M. Pinelli and M. Venturini, “Application of methodologies to evaluate the health state of gas turbines in a cogenerative combined cycle power plant,” in Proceedings of the Turbine Technical Conference and Exposition, ASME Paper GT-2002-30248, pp. 187–195, June 2002.
[66]  ISO 2314, “Gas Turbine—Acceptance Tests,” International Standard, 1989.
[67]  I. S. Diakunchak, “Performance deterioration in industrial gas turbines,” Journal of Engineering for Gas Turbines and Power, vol. 114, no. 2, pp. 161–168, 1992.
[68]  H. I. H. Saravanamuttoo and B. D. Maclsaac, “Thermodynamic models for pipeline gas turbine diagnostics,” Journal of Engineering for Power, vol. 105, no. 4, pp. 875–884, 1983.

Full-Text

comments powered by Disqus