Diagnosis methodology for the turbocharger groups installed on a 1 MW internal combustion engine

Applied Energy

Volumn 86, Issue 12, 2009, Pages 2721-2730

  Barelli, L ^a   Bidini, G ^a   Bonucci, F ^a  

^a UNIVERSITY OF PERUGIA   (Italy)

Author keywords

Artificial Neural Networks; ICE diagnosis; Turbocharger group simulation

Indexed keywords

COMBUSTION; COMPRESSORS; DEEP NEURAL NETWORKS; ENERGY CONVERSION; ENGINES; ICE CONTROL; INTERNAL COMBUSTION ENGINES; NEURAL NETWORKS; SUPERCHARGERS; TURBOMACHINERY;

EXPERIMENTAL CAMPAIGN; FUNDAMENTAL CHARACTERISTICS; ICE DIAGNOSIS; NEURAL ARCHITECTURES; OPERATION CONDITIONS; OPERATIONAL STATE; POLLUTING EMISSION; TURBOCHARGER GROUP SIMULATION;

ICE;

ARTIFICIAL NEURAL NETWORK; COMBUSTION; EMISSION CONTROL; ENERGY EFFICIENCY; ENGINE; EXPERIMENTAL STUDY; INSTALLATION; SIMULATION;

EID: 68849113419     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2009.04.034     Document Type: Article

References (17)

1. Barelli L., and Bidini G. Design of the measurements validation procedure and the expert system architecture for a cogeneration internal combustion engine. Appl Therm Eng 25 (2005) 2698-2714
2. Patton R., Frank P., and Clark R. Fault diagnosis in dynamic systems-theory and application (1989), Prentice Hall International (UK) Ltd.
3. Fantozzi F., and Desideri U. LO.CO.MO.DIA low cost monitoring for diagnostic purposes of a small scale CHP plant. Proc ASME Adv Energy Syst Div, AES 40 (2000) 471-479
4. Clark R.N., and Campbell B. Instrument fault detection in a pressurized water reactor pressuriser. Nucl Technol 56 (1982) 23-32
5. Cifaldi ML, Chokani N. Engine monitoring using neural networks. Paper no. AIAA 98-3548; 1998.
6. Green A, Allen D. Artificial intelligence for real-time diagnostics and prognostics of gas turbine engines. Paper no. AIAA 97-2899, 33rd JPC & Exhibition; 1997.
7. Kenelopoulos K, Stamatis A, Mathoiudakis K. Incorporating neural networks into gas-turbine performance diagnostics, IGTI. Paper no. 97-GT-35, Orlando, FL; 1997.
8. Wrest DJ, Hines JW, Uhrig RE. Instrument surveillance and calibration verification through plant wide monitoring using autoassociative neural networks. In: Proceedings of 1996 ANSITMNPI; 1996.
9. Zedda M, Singh R. Fault diagnosis of a turbofan engine using neural networks: a quantitative approach. Paper no. AIAA 98-3602, 34th JPC & Exhibition, 1998.
10. Bidini G, Fantozzi F, Barelli L. Thermodynamic diagnosis of a small scale gas turbine with neural networks. In: Proceedings of 2001 ASME international mechanical engineering congress and exposition, November 11-16, 2001, New York, NY, AES, 41; 2001. p. 441-6.
11. Torella G. Expert systems and neural networks for fault isolation in gas-turbines. International society of air-breathing engines, ISABE 97-7148; 1997.
12. DePold H.R., and Gass F.D. The application of expert systems and neural networks to gas turbine prognostics and diagnostics. J Eng Gas Turb Power 121 October (1999) 607-612
13. Mesbahi E. An intelligent sensor validation and fault diagnostic technique for diesel engine. In: Proceedings of ECOS 2002, July 3-5, Berlino, Germania.
14. Biagetti T, Melli R, Sciubba E, Balducelli C, Bologna S, Iannucci R, et al. PROMISE: un Prototipo di Sistema Esperto per la Diagnostica, Prognostica e Gestione Assistita di Impianti Cogenerativi, X Convegno, Tecnologie e Sistemi Energetici Complessi; 2001 [in Italian].
15. Tulunay Y., Tulunay E., and Senalp E.T. The neural network technique-1: a general exposition. Adv Space Res 33 (2004) 983-987
16. Walczak S., and Cerpa N. Heuristic principles for the design for the artificial neural networks. Inf Softw Technol 41 (1999) 107-117
17. Barelli L. Diagnostica termodinamica di turbine a gas con reti neurali. MSc Thesis, University of Perugia, Perugia, Italy; 2000 [in Italian].