EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 74 (2016) MATEC Web Conf., 74 (2016) 00005 References
Open Access
Issue
MATEC Web Conf.
Volume 74, 2016
The 3rd International Conference on Mechanical Engineering Research (ICMER 2015)
Article Number 00005
Number of page(s) 8
DOI https://doi.org/10.1051/matecconf/20167400005
Published online 29 August 2016
  1. G. Vachtsevanos et al., Intelligent Fault Diagnosis and Prognosis for Engineering Systems, 1–434, (2007). [CrossRef] [Google Scholar]
  2. C.B. Meher-Homji, A. Bromley, Gas Turbine Axial Compressor Fouling and Washing 33rd Turbomach. Symp., Texas A & M University, (2004). [Google Scholar]
  3. Marco Mucino and Y. G. Li, A diagnostic system for gas turbines using GPA-Index, COMADEM-C007-(2005). [Google Scholar]
  4. I. Loboda, Gas Turbine Condition Monitoring and Diagnostics, National Politechnique Institute, Mexico, (2010). [Google Scholar]
  5. L. Marinai, D. Probert, and R. Singh, Prospects for aero gas-turbine diagnostics: A review. Appl. Energy, 79(1): p. 109–126, (2004). [CrossRef] [Google Scholar]
  6. S.O.T. Ogaji, R. Singh, Advanced engine diagnostics using artificial neural networks. Appl. Soft Comput., 3(3): p. 259–271, (2003). [CrossRef] [Google Scholar]
  7. L. Marinai, Gas-path diagnostics and prognostics for aero-engines using fuzzy logic and Time series analysis” Cranfield University, PhD thesis, UK, (2004). [Google Scholar]
  8. Y.G. Li, Performance-analysis-based gas turbine diagnostics: A review. Proceedings of the Institution of Mechanical Engineers, Part A: Int. J. Power Energy Syst, 216(5): p. 363–377, (2002). [CrossRef] [Google Scholar]
  9. Z.N.S. Vanini, K. Khorasani, N. Meskin, Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach. Information Sciences, 259(0): p. 234–251, (2014). [CrossRef] [Google Scholar]
  10. C. Romesis, Y.G. Li, Condition based maintenance (CBM) for gas turbines plants, Laboratory of Thermal Turbomachines National Technical University of Athens, Greec & Department of Power and Propulsion, Cranfield University, UK, (2013). [Google Scholar]
  11. Z.N.S. Vanini, N. Meskin, K. Khorasani, Multiple-model sensor and components fault diagnosis in gas turbine engines using autoassociative neural networks. J. Eng. Gas Turbines Power, 136(9), (2014). [Google Scholar]
  12. m. Fast, M. Assadi, S. De, Development and multi-utility of an ANN model for an industrial gas turbine. Appl. Energy, 86(1):p. 9–17, (2009). [CrossRef] [Google Scholar]
  13. D. An, N.H. Kim, J.H. Choi, Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliability Engineering and System Safety, 133: p. 223–236, (2015). [Google Scholar]
  14. A.K.S Jardine, D. Lin, D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Sig. Process., 20(7): p. 1483–1510, (2006). [Google Scholar]
  15. R. Verma, N. Roy, R. Ganguli, Gas turbine diagnostics using a soft computing approach. Appl. Math. Comput., 172(2 SPEC. ISS.): p. 1342–1363, (2006). [CrossRef] [Google Scholar]
  16. L.A. Urban, Gas Turbine Engine Parameter Interrelationships, HSD UTC, Windsor Locks, Ct., 1st edition, 1967, 2nd edition, (1969). [Google Scholar]
  17. L.A. Urban, Parameter Selection for Multiple Fault Diagnostics of Gas Turbine Engines. J Eng Power Trans ASME, 97 Ser A(2): p. 225–230, (1975). [CrossRef] [Google Scholar]
  18. D.L. Doel, TEMPER – A Gas Path Analysis Tool for Commercial Jet Engines, J. Eng. Gas Turbines Power, Vol. 116, No. 1, pp.82–89, (1994). [CrossRef] [Google Scholar]
  19. Volponi, The use of optimal estimation techniques in the analysis of gas turbines PhD thesis, Cranfield Univerrsity, UK, (1994). [Google Scholar]
  20. P. C. Escher, Pythia: An object-oriented Gas Path Analysis Computer Program for General Applications, PhD thesis, Cranfield University, UK, (1995). [Google Scholar]
  21. D.L. Doel, TEMPER - a gas-path analysis tool for commercial jet engines. J. Eng. Gas Turbines Power, 116 (1):p. 82–89, (1994). [CrossRef] [Google Scholar]
  22. A. Stamatis, K. Mathioudakis, K. Papailiou, Optimal measurement and health index selection for gas turbine performance status and fault diagnosis. J. Eng. Gas Turbines Power, 114(2): p. 209–216, (1992). [CrossRef] [Google Scholar]
  23. Y.G. Li, Gas Turbine Performance and Health Status Estimation Using Adaptive Gas Path Analysis. J. Eng. Gas Turbines Power, 132(4): p. 041701–041701, (2010). [Google Scholar]
  24. A.G. Stamatis, Evaluation of gas path analysis methods for gas turbine diagnosis. J. Mech. Sci. Technol., 25(2): p. 469–477, (2011). [CrossRef] [Google Scholar]
  25. C. Kong, Review on advanced health monitoring methods for aero gas turbines using model based methods and artificial intelligent methods. Int. J. Aeronaut. Space Sci., 15(2): p. 123–137, (2014). [CrossRef] [Google Scholar]
  26. Emil Larsson, Model Based Diagnosis and Supervision of Industrial Gas Turbines, Linköping University, PhD thesis, Sweden, (2014). [Google Scholar]
  27. M.S. Jasmani, Y.G. Li, Z. Ariffin, Measurement selections for multicomponent gas path diagnostics using analytical approach and measurement subset concept. J. Eng. Gas Turbines Power, 133(11), (2011). [Google Scholar]
  28. M. Chen, H. L. Quan, H. Tang, An Approach for Optimal Measurements Selection on Gas Turbine Engine Fault Diagnosis. ASME. J. Eng. Gas Turbines Power.;137(7): 071203–071203-9. doi:10.1115/1.4029171, (2015). [Google Scholar]
  29. B. Paul, A. Konar, A. K. Mandal, Fuzzy ADALINEs for gray image recognition, Neurocomputing, vol. 24, pp. 207–223, (1999). [CrossRef] [Google Scholar]
  30. A. Konar, Artificial intelligence and soft computing: behavioral and cognitive modeling of the human brain, ISBN: 978-0-9778582-3-1, India, (2008). [Google Scholar]
  31. M. Kubat, Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7. The Knowledge Engineering Review, (2003). [Google Scholar]
  32. R. Ganguli, Gas Turbine Diagnostics Signal Processing and Fault Isolation, CRC Press (2013) Print ISBN: 978-1-4665-0272-7. [Google Scholar]
  33. G. Denney, F16 jet engine trending and diagnostics with neural networks. Proc. SPIE 1965, Applications of Artificial Neural Networks IV, 419 (September 2, 1993); doi:10.1117/12.152545. [CrossRef] [Google Scholar]
  34. S.O.T. Ogaji, Advanced Gas-path Fault Diagnostics for Stationary Gas Turbines, Cranfield University, PhD thesis, UK, (2003). [Google Scholar]
  35. S.O.T. Ogaji, R. Singh, S.D. Probert, Multiple-sensor fault-diagnoses for a 2-shaft stationary gas-turbine. Applied Energy, 71(4): p. 321–339, (2002). [CrossRef] [Google Scholar]
  36. G.R. Matuck, et al. Multiple faults detection of gas turbine by MLP neural network. in Proceedings of the ASME Turbo Expo, (2009). [Google Scholar]
  37. S. Sampath, R. Singh, An integrated fault diagnostics model using genetic algorithm and neural networks. J. Eng. Gas Turbines Power, 128(1): p. 49–56, (2006). [CrossRef] [Google Scholar]
  38. K. Patan, M. Witczak, J. Korbicz, Towards robustness in neural network based fault diagnosis. Int. J. Appl. Math. Comput. Sci., 18(4): p. 443–454, (2008). [CrossRef] [Google Scholar]
  39. R. Mohammadi, et al. Fault diagnosis of gas turbine engines by using dynamic neural networks. in Midwest Symposium on Circuits and Systems. (2011). [Google Scholar]
  40. T.B.S. Sina, Z.N.S. Vanini, K. Khorasani, Dynamic neural network-based fault diagnosis of gas turbine engines. Neurocomputing, 125(0): p. 153–165, (2014). [CrossRef] [Google Scholar]
  41. P. Fuster, A. Ligeza, J. Aguilar Martin, adductive diagnostic procedure on an AND/OR/NOT graph for expert behaviour: Application to a gas turbine, 10th international congress and exhibition on condition monitoring and diagnostic [Google Scholar]
  42. R. Ganguli, Data rectification and detection of trend shifts in jet engine path measurements using median filters and fuzzy logic. J. Eng. Gas Turbines Power, 124(4): p. 809–816, (2002). [CrossRef] [Google Scholar]
  43. R. Ganguli, Application of fuzzy logic for fault isolation of jet engines. J. Eng. Gas Turbines Power, 2003. 125(3): p. 617–623. [CrossRef] [Google Scholar]
  44. S.O.T. Ogaji, et al., Gas-turbine fault diagnostics: a fuzzy-logic approach. Appl. Energy, 82(1): p. 81–89, (2005). [CrossRef] [Google Scholar]
  45. A. Kyriazis, et al. Gas turbines compressor fault identification by utilizing fuzzy logic-based diagnostic systems. in 9th European Conference on Turbomachinery: Fluid Dynamics and Thermodynamics, ETC 2011 - Conference Proceedings. (2011). [Google Scholar]
  46. Y.G. Li, An Genetic Algorithm Approach to Estimate Performance Status of Gas Turbine, ASME Paper No. ASME GT2008-50175, (2008). [Google Scholar]
  47. M. Zedda, Gas-turbine engine and sensor fault diagnosis, PhD Thesis, School of Mechanical Engineering, Cranfield University, UK, (1999). [Google Scholar]
  48. C.D. Kong, M.C. Kang, G.L. Park, Study on Condition Monitoring of 2-Spool Turbofan Engine using Non-Linear Gas Path Analysis Method and Genetic Algorithms, IJMMM, Vol. 1, No. 2, pp. 214–220, (2013). [CrossRef] [Google Scholar]
  49. H. Abbasi Nozari, et al., Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques. Neurocomputing, 91(0): p. 29–47, (2012). [CrossRef] [Google Scholar]
  50. Aiad Gannan, Cascade Testing and CFD Applied to Gas Turbine Performance Improvement with Compressor Cleaning, Cranfield University, PhD thesis, UK, (2012). [Google Scholar]
  51. A.L. Tamiru, Intelligent Fault Detection and Diagnosis System for A Cogeneration And Cooling Plant., Universiti Teknologi PETRONAS, PhD Thesis, Malaysia, (2012). [Google Scholar]
  52. B. Shahriari, A. N. Shahrbabaki, A. Shahriari, “Gas Turbine Fault Detection and Isolation Using Adaptive Neurofuzzy Inference System (ANFIS)”, Advanced Materials Research, Vol. 1016, pp. 721–725, Aug. (2014). [CrossRef] [Google Scholar]
  53. R. Bettocchi, M. Pinelli, P. R. Spina, and M. Venturini, “Artificial Intelligence for the Diagnostics of Gas Turbines—Part II Neural-Fuzzy Approach,” J. Eng. Gas Turbines Power. Vol. 129, No. 3;,pp.720–729, (2007). [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.