Multiresolution analysis of vibration signals acquired from locomotive Diesel engine for classification of engine states basing on signal statistical parameters

• Autorzy: Boguś P. , Cieszyński M. , Merkisz J.

Identyfikatory

DOI

10.19206/CE-2017-111

• Języki publikacji: EN

Abstrakty

EN

The paper presents a method of classification of locomotive Diesel engine states basing on vibration signals taken from an engine body and using chosen statistical parameters calculated for the original signal and it wavelet multiresolution components. The researches presented in the paper concern estimation of an engine states before and after a general repair. The target application of the presented researches is an on-line diagnostic system which can complement standard OBD systems. To this purpose the applied methods should not base on complex analysis of some spectral, time-frequency or scalogram plots but rather on choosing single diagnostic parameters which are suitable for the fast on-line diagnostic. The results have showed the significant difference in distinguishing of engine work before and after a general repair using some chosen statistical parameters applied to vibration signals.

Słowa kluczowe

EN

vibration signals; Diesel engine diagnostic; multiresolution analysis; classification

PL

sygnały drganiowe; silnik spalinowy; analiza wielorozdzielcza; klasyfikacja

• Wydawca: Polskie Towarzystwo Naukowe Silników Spalinowych
• Czasopismo: Combustion Engines
• Rocznik: 2017
• Tom: R. 56, nr 1
• Strony: 68--72
• Opis fizyczny: Bibliogr. 14 poz., il., wykr.

Twórcy

autor

Boguś P.

• Department of Physics and Biophysics of Medical University of Gdańsk, Rail Vehicle Institute TABOR in Poznań

autor

Cieszyński M.

• Department of Physics and Biophysics of Medical University of Gdańsk

autor

Merkisz J.

• Faculty of Machines and Transport at Poznan University of Technology

Bibliografia

• [1] MERKISZ, J. Ecological aspects of combustion engines (Part 1 and 2). Technical University of Poznań Publisher, Poznań, Poland, 1998 and 1999 (in Polish).
• [2] ABARBANEL, H.D.I. Analysis of observed chaotic data. Springer, 1996.
• [3] BOGUŚ, P., MERKISZ, J. Short-time analysis of combustion engine vibroacoustic signals through pattern recognition techniques. SAE Technical Paper Series (2005), 2005-01-2529.
• [4] BOGUŚ, P., MERKISZ, J. Misfire detection of locomotive diesel engine by nonlinear analysis. Mechanical Systems and Signal Processing. 2005, 19, 881-899.
• [5] BOGUŚ, P., MERKISZ, J. Wavelets application in combustion engine diagnostic. Combustion Engines. 2013, 154(3), 226-231.
• [6] BOGUŚ, P., DEDO, M., GRZESZCZYK, R., WRONA, A., MARKOWSKI, J., MERKISZ, J. Estimation of fuel spraying from diesel engine injector using multiresolution wavelet analysis of vibroacoustic signals. Combustion Engines. 2015, 162(3), 264-270.
• [7] GOSWAMI, J.C., CHAN, A.K. Fundamentals of wavelets. theory, algorithms, and applications. John Wiley & Sons, 2010.
• [8] MITRA, S.K., KAISER, J.F. (eds.) Handbook for digital signal processing. John Wiley & Sons. 1993.
• [9] SMITH, S.W. Digital signal processing: a practical guide for engineers and scientists. BTC. Warszawa, 2003 (in Polish).
• [10] CHUI, K. Wavelets: A mathematical tool for signal processing. SIAM Society for Industrial and Applied Mathematics. 1997.
• [11] AL-BADOUR, F., SUNAR, M., CHEDED, L. Vibration analysis of rotating machinery using time–frequency analysis and wavelet techniques. Mechanical Systems and Signal Processing. 2011, 25, 2083-2101.
• [12] PENG, Z.K., CHU, F.L. Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing. 2004, 18, 199-221.
• [13] TSEA, P.W., YANGB, W., TAMA, H.Y. Machine fault diagnosis through an effective exact wavelet analysis. Journal of Sound and Vibration. 2004, 277, 1005-1024.
• [14] BOGUŚ, P., SIENICKI, A., WOJCIECHOWSKA, E., MERKISZ, J. The comparison of vibroacoustic signals taken from an engine before and after repair. Combustion Engines, 2007-SC3, 300-306.