Abstract
Currently applied diagnostic systems of technical condition use more and more advanced methods of gathering and processing input data. Many scientific research centres all over the world deal with problems connected with this topic. Up till now no such unified set of guidelines has been constructed which would allow for construction of properly functioning diagnostic system no matter which object is chosen. That is why there is a constant need to test the possibilities of application of existing methods and their modification to match given diagnostic cases. In this article the results of tests conducted with the use of Bayes classifier for diagnostic purposes are presented. The purpose of the tests was diagnosis of technical condition of head gasket in internal combustion engine of a car. The source of information about technical condition was the vibration signal measured in various measurement points. In order to describe the character of changes occurring in vibration signal the measurement in form of entropy was marked for decomposed signal with the use of discrete wavelet transform (DWT).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Christensen, R., et al.: Bayesian ideas and data analysis: an introduction for scientists and statisticians. CRC Science (2010)
Czech, P., Madej, H.: Application of spectrum and spectrum histograms of vibration engine body for setting up the clearance model of the piston-cylinder assembly for RBF neural classifier. EksploatacjaiNiezawodność - Maintenance And Reliability 4 (2011)
Dąbrowski, D., Cioch, W.: Neural classifiers of vibroacoustic signals in implementation on programmable devices (FPGA) – Comparison. ActaPhysicaPolonica, 119(6-A) (2011)
Dybała, J., Zimroz, R.: Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal. Applied Acoustics 77 (2014)
Figlus, T.: Diagnosing the engine valve clearance, on the basis of the energy changes of the vibratory signal. Maintenance Problems 1 (2009)
Grega, R., et al.: The analyse of vibrations after changing shaft coupling in drive belt conveyer. ZeszytyNaukowePolitechnikiŚląskiej. Transport 72 (2011)
Grządziela, A.: Diagnosis of naval gas turbine rotors with the use of vibroacousticpapmeters. Polish Maritime Researches 7(3) (2000)
Kasprzak, W.: Recognition of pictures and voice signals. OficynaPolitechnikiWarszawskiej, Warszawa (2009)
Korbicz, J., et al.: Fault diagnosis, Models, Artificial Intelligence, Applications. Springer (2004)
Kwiatkowski, W.: Methods of automatic recognition of models. BEL Studio, Warszawa (2007)
Medvecká-Beňová, S., Vojtková, J.: Analysis of asymmetric tooth stiffness in eccentric elliptical gearing. Technolog 5(4) (2013)
Mikulski, J. (ed.): TST 2013. CCIS, vol. 395. Springer, Heidelberg (2013)
Mikulski, J.: Introduction of telematics for transport. In: Proceedings on 9th International Conference ELEKTRO 2012, Rajecke Teplice, IEEE Catalog Number CFP1248S-ART, May 21-22, pp. 336–340 (2012), ieexplore.ieee.org
Osowski, S.: Methods and tools for data exploration. Wydawnictwobtc, Legionowo (2013)
Puškár, M., Bigoš, P., Puškárová, P.: Accurate measurements of output characteristics and detonations of motorbike high-speed racing engine and their optimization at actual atmospheric conditions and combusted mixture composition. Measurement 45 (2012)
Tadeusiewicz, R., Chaki, R., Chaki, N.: Exploring Neural Networks with C#. CRC Press, Taylor & Francis Group, Boca Raton (2014)
Tadeusiewicz, R., et al. (eds.): Neural Networks in Biomedical Engineering. Biomedical Engineering. Basics and Applications. Exit, Warsaw (2013)
Urbanský, M., Homišin, J., Krajňák, J.: Analysis of the causes of gaseous medium pressure changes in compression space of pneumatic coupling. Transactions of the Universities of Košice, 2 (2011)
Zuber, N., Bajrić, R., Šostakov, R.: Gearbox faults identification using vibration signal analysis and artificial intelligence methods. EksploatacjaiNiezawodnosc - Maintenance and Reliability 16(1) (2014)
Żółtowski, B., Cempel, C.: Machine diagnostics engineering. ITE, Radom (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Czech, P., Mikulski, J. (2014). Application of Bayes Classifier and Entropy of Vibration Signals to Diagnose Damage of Head Gasket in Internal Combustion Engine of a Car. In: Mikulski, J. (eds) Telematics - Support for Transport. TST 2014. Communications in Computer and Information Science, vol 471. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45317-9_24
Download citation
DOI: https://doi.org/10.1007/978-3-662-45317-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45316-2
Online ISBN: 978-3-662-45317-9
eBook Packages: Computer ScienceComputer Science (R0)