Analysis of vibration signals using short-time analysis and clustering in parameter space for detection of combustion engine state

• Autorzy: Boguś Piotr , Merkisz Jerzy

Identyfikatory

DOI

10.19206/CE-2019-214

• Języki publikacji: EN

Abstrakty

EN

The paper presents a short-time analysis of the vibration signals for the diagnosis of Diesel engine of combustion locomotive by recognition of different engine states using the clustering technique. The main aim of the researches was to distinguish between different engine states represent different wear extends. The proposed method of vibration signal analysis consists on sliding a time window along signal in time and observing the changes of some given statistical parameters. The set of this parameter values creates a multidimensional parameter space where the time evolution can be observed. For recognition and detection of different engine system states some clustering techniques in the parameter space were performed. The results show the possibility of distinguishing different cluster centers within the parameter space which can be assigning to different engine states represented the states before and after a general repair.

Słowa kluczowe

EN

vibration signals; Diesel engine diagnostic; short-time analysis; clustering

PL

sygnały drganiowe; diagnostyka silników wysokoprężnych; analiza krótkoczasowa; klasteryzacja

• Wydawca: Polskie Towarzystwo Naukowe Silników Spalinowych
• Czasopismo: Combustion Engines
• Rocznik: 2019
• Tom: R. 58, nr 2
• Strony: 83--87
• Opis fizyczny: Bibliogr. 17 poz., 1 fot. kolor., wykr.

Twórcy

autor

Boguś Piotr

• Rail Vehicle Institute TABOR in Poznań, Poland

autor

Merkisz Jerzy

• Faculty of Transport Engineering, Poznan University of Technology

Bibliografia

• [1] ABE, S. Support vector machines for pattern classification. Springer-Verlag. 2005.
• [2] ALLEN, J.B., RABINER. A unified approach to short-time Fourier analysis and synthesis. Proceedings of the IEEE. 1977, 65, 1558-1564.
• [3] ALLEN, J.B. Short term spectral analysis, synthesis, and modification by Discrete Fourier Transform. IEEE Transactions on Acoustic, Speech, and Signal Processing ASSP-25. 1977, 235-238.
• [4] BEZDEK, J.C. Pattern recognition with fuzzy objective function algorithms. Plenum Press, Second edition, 1987.
• [5] BOGUŚ, P., LEWANDOWSKA, K. Short-time signal analysis using pattern recognition methods. Artificial Intelligence and Soft Computing. 2004, 3070, 550-555.
• [6] BOGUŚ, P., MERKISZ, J. Short-time analysis of combustion engine vibroacoustic signals through pattern recognition techniques. SAE Technical Paper. 2005, 2005-01-2529.
• [7] BOGUŚ, P., MERKISZ, J. Misfire detection by short-time analysis with using clustering techniques. Congress Proceedings, PTNSS KONGRES 2005, September 25th - 28th, 2005, Bielsko-Biała/Szczyrk.
• [8] 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.
• [9] BOGUŚ, P., SIENICKI, A., WOJCIECHOWSKA, E. Porównanie stanu silnika lokomotywy spalinowej ST44 przed i po remoncie przy użyciu sygnału wibroakustycznego. Pojazdy Szynowe. 2007, 2, 28-36.
• [10] BOGUŚ, P., MERKISZ, J., MAZUREK, S. The prospects of artificial intelligence methods in identification and prevention of critical railway accidents. L. Rutkowski, R. Tadeusiewicz, L. Zadeh, J. Zurada (eds.). Computational Intelligence: Methods and Applications. EXIT, Warsaw 2008, 445-453.
• [11] BOGUŚ, P., GRZESZCZYK, R., WRONA, A. et al. Estimation of fuel spraying from diesel engine injector using multiresolution wavelet analysis of vibroacoustic signals. Combustion Engines. 2015, 162(3), 264-270.
• [12] DUDA, R., HART, P. Pattern classification and scene analysis. New York, Wiley Interscience 1973.
• [13] HARRIS, F.J. On the Use of windows for harmonic analysis with the Discrete Fourier Transform. Proceedings of the IEEE. 1978, 66, 51-83.
• [14] MERKISZ, J. Ecological aspects of combustion engines (Part 1 and 2). Poznań: Wydawnictwo Politechniki Poznańskiej. 1998 and 1999 (in Polish).
• [15] PORTNOFF, M.R. Time-frequency representation of digital signals and systems based on Short-Time Fourier Analysis. IEEE Transactions on Acoustic, Speech, and Signal Processing. 1980, 28, 55-69.
• [16] WANG, L. (ed.). Support vector machines: theory and applications. Springer-Verlag 2005.
• [17] YANG, J., PU, L. et al. Fault detection in a diesel engine by analyzing the instantaneous angular speed. Mechanical Systems and Signal Processing. 2001, 15, 549-564.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).