Browse > Article
http://dx.doi.org/10.1016/j.ijnaoe.2017.09.012

Marine gas turbine monitoring and diagnostics by simulation and pattern recognition  

Campora, Ugo (Dipartimento di Ingegneria Meccanica, Energetica, Gestionale, Trasporti (DIME), University of Genoa, Polytechnic School)
Cravero, Carlo (Dipartimento di Ingegneria Meccanica, Energetica, Gestionale, Trasporti (DIME), University of Genoa, Polytechnic School)
Zaccone, Raphael (Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni (DITEN), University of Genoa, Polytechnic School)
Publication Information
International Journal of Naval Architecture and Ocean Engineering / v.10, no.5, 2018 , pp. 617-628 More about this Journal
Abstract
Several techniques have been developed in the last years for energy conversion and aeronautic propulsion plants monitoring and diagnostics, to ensure non-stop availability and safety, mainly based on machine learning and pattern recognition methods, which need large databases of measures. This paper aims to describe a simulation based monitoring and diagnostic method to overcome the lack of data. An application on a gas turbine powered frigate is shown. A MATLAB-SIMULINK(R) model of the frigate propulsion system has been used to generate a database of different faulty conditions of the plant. A monitoring and diagnostic system, based on Mahalanobis distance and artificial neural networks have been developed. Experimental data measured during the sea trials have been used for model calibration and validation. Test runs of the procedure have been carried out in a number of simulated degradation cases: in all the considered cases, malfunctions have been successfully detected by the developed model.
Keywords
Monitoring and diagnostics; Artificial neural networks; Ship simulation; Ship propulsion; Gas turbines;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Altosole, M., Martelli, M., 2017. Propulsion control strategies for ship emergency manoeuvres. Ocean. Eng. 137(2017), 99-109.   DOI
2 Altosole, M., Benvenuto, G., Campora, U., 2010. Numerical modelling of the engines governors of a CODLAG propulsion plant. In: 20th BLACK-SEA International Congress, Varna, Bulgaria, 7-9 October.
3 Altosole, M., Martelli, M., Vignolo, S., 2012a. A mathematical model of the propeller pitch change mechanism for the marine propulsion control design. In: Rizzuto, Soares, Guedes (Eds.), Sustainable Maritime Transportation and Exploitation of Sea Resources, vol. II, pp. 649-656.
4 Altosole, M., Figari, M., Martelli, M., 2012b. Time domain simulation for marine propulsion applications. In: Proceedings of the 2012-Summer Computer Simulation Conference, SCSC 2012, Part of SummerSim 2012 Multiconference. Genoa, Italy, 8-11 July.
5 Altosole, M., Campora, U., Martelli, M., Figari, M., 2014. Performance decay analysis of a marine gas turbine propulsion system. J. Ship Res. 58(3) https://doi.org/10.5957/JOSR.58.3.130037. September 2014, pp. 117-129, ISSN:0022-4502.   DOI
6 Benvenuto, G., Campora, U., 2005. A gas turbine modular model for ship propulsion studies. In: HSMV, 7th Symposium on High Speed Marine Vehicles. Naples, Italy, 21 - 23 September.
7 Benvenuto, G., Campora, U., Carrera, G., Figari, M., 2000. Simulation of ship propulsion plant dynamics in rough sea. In: $8^{\circ}$ International Conference on Marine Engineering Systems (ICMES/SNAME 2000). New York, USA, May 22-23.
8 Bidini, G., Grimaldi, C.N., Mariani, F., 1998. Un approccio non convenzionale per la diagnosi di motori di notevole dimensione. In: $53^{\circ}$ Congresso Nazionale A.T.I. Florence, Italy, September 15-18.
9 Campora, U., Carretta, M., Cravero, C., 2013. Performance decay simulation of a gas turbine for helicopter propulsion. Transaction Control Mech. Syst. 2, 105-114. March.
10 Campora, U., Capelli, M., Cravero, C., Zaccone, R., 2015. The development of metamodels of a gas turbine powered marine propulsion system for simulation and diagnostic purposes. J. Nav. Archit. Mar. Eng. 12(No 1, June), 1-14. https://doi.org/10.3329/jname.v12i1.19719. ISSN 1813-8535 (Print), 2070-8998 (Online).   DOI
11 Coraddu, A., Oneto, L., Ghio, A., Savio, S., Anguita, D., Figari, M., 2016. Machine learning approaches for improving condition-based maintenance of naval propulsion plants. Proc. Institution Mech. Eng. Part M J. Eng. Marit. Environ. 230(1), 136-153.
12 Cohen, H., Rogers, G.F.C., Saravanamuttoo, H.I.H., 1987. Gas Turbine Theory, third ed. Longman Scientific & Technical, Harlow, Essex, England.
13 Haykin, S., 1998. Neural Networks: a Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River, NJ, USA. ISBN:0132733501.
14 Izadi-Zamanabadi, R., Blanke, M., Katebi, S., 2001. Cheap Diagnosis Using Structural Modeling and Fuzzy Logic Based Detection. Elsevier. August 19.
15 Kramer, M.A., 1991. Nonlinear principal component analysis using auto-associative neural networks. AIChE J. 37 (February), 233-243.   DOI
16 Kumano, S., Aoyama, K., Mikami, N., 2011. Advanced Gas Turbine Diagnostics Using Pattern Recognition. ASME paper GT2011-45670.
17 Li, Y.G., 2002. Performance analysis based gas turbine diagnostics: a review. Proc. Institution Mech. Eng. Par. A; J. Power Energy 216 (5, September), 363-377.   DOI
18 Li, P., Su, B., 2008. On the intelligent fault diagnosis methods for marine diesel engine. In: ICACIA International Conference on Apperceiving Computing and Intelligence Analysis, Chengdu, pp. 397-400. E_ISBN: 978-1-4244-3426-8.
19 Martelli, M., Figari, M., Altosole, M., Vignolo, S., 2014b. Controllable pitch propeller actuating mechanism, modelling and simulation. J. Eng. Marit. Environ. 228(1), 29-43.
20 Martelli, M., Viviani, M., Altosole, M., Figari, M., Vignolo, S., 2014a. Numerical modelling of propulsion, control and ship motions in 6 degrees of freedom. J. Eng. Marit. Environ. 228(4), 373-397.
21 Michetti, S., Ratto, M., Spadoni, A., Figari, M., Altosole, M., Marcilli, G., 2010. Ship Control system wide integration and the use of dynamic simulation techniques in the Fremm project. In: Proceedings of the International Conference on Electrical Systems for Aircraft, Railway and Ship Propulsion, ESARS'10, Bologna, Italy, October 19-21.
22 Ogaji, S.O.T., Marinai, L., Sampath, S., Singh, R., Prober, S.D., 2005. Gas-turbine fault diagnostics: a fuzzy logic approach. Appl. Energy 82(1), 81-89.   DOI
23 Palme, T., Breuhaus, P., Assadi, M., Klein, A., Kim, M., 2011a. New Alstom Monitoring Tools Leveraging Artificial Neural Network Technologies. ASME paper GT2011 - 45990.
24 Palme, T., Breuhaus, P., Assadi, M., Klein, A., Kim, M., 2011b. Early Warning of Gas Turbine Failure by Nonlinear Feature Extraction Using an Auto-associative Neural Network Approach. ASME paper GT2011-45991.
25 Pawletko, R., 2005. The use of neural networks for the faults classification of a marine diesel engine fuel injection system, UICEE. Glob. J. Engng. Educ. 9(9) published in Australia.
26 Rigoni, E., Lovision, A., 2007. Automatic Sizing of Neural Network for Function Approximation. International Conference on Systems, Man and Cybernetics, ISIC, IEEE, Montreal, Canada.
27 Stamatis, A.G., 2011. Evaluation of gas path analysis methods for gas turbine diagnosis. J. Mech. Sci. Technol. 25(2), 469.   DOI
28 Zaccone, R., 2013. Monitoraggio di un impianto di propulsione navale con turbina a gas mediante simulazione e metamodelli. Marine Engineer & Naval Architect Graduate Thesis. University of Genoa, Politechnic School, Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni (DITEN), Italy. July 19, in Italian.
29 Urban, L.A., 1969. Gas turbine engine parameter interrelationships. In: Hamilton Standard Division of United Aircraft Corporation, second ed.
30 Verbist, M.L., Visser, W.P.J., P van Bujitenen, J., Dulvis, R., 2011. Gas Path Analysis on KLM I In-flight Engine Data. ASME paper GT2011-45625.
31 Zaccone, R., Altosole, M., Campora, U., Figari, M., 2015. Diesel engine and propulsion diagnostics of a mini-cruise ship by using artificial neural networks. In: Proc. Of the 16th International Congress of the International Maritime Association of the Mediterranean, Pula (Croatia), 21-24 September.
32 Fentaye, A.D., Gilan, S.I.U.H., Baheta, A.T., 2016. Gas turbine gas path diagnostics: a review. In: MATEC Web of Conferences, vol. 74. EDP Sciences, p. 00005.
33 Taguchi, G., Jugulum, R., 2002. The Mahalanobis - Taguchi Strategy: a Pattern Technology System, fifth ed. John Wiley & Sons. ISBN: 978-0-471-02333-3.