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Engine valve clearance diagnostics based on vibration signals and machine learning methods

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Diagnostyka luzu zaworów silnika spalinowego z wykorzystaniem sygnału drganiowego i metod uczenia maszynowego
Języki publikacji
EN PL
Abstrakty
EN
A dynamic advancement of the design of combustion engines generates a necessity of introduction of strategies of operation based on the information related to their technical condition. The paper analyzes problems related to vibration based diagnostics of valve clearance of a piston combustion engine, significant in terms of its efficiency and durability. Methods of classification have been proposed for the assessment of the valve clearance. Experiments have been performed and described that aimed at providing information necessary to develop and validate the proposed methods. In the performed investigations, the vibration signals were obtained from a triaxial accelerometer located in the engine cylinder head. A parameterization of the obtained vibration signal has been carried out for the engine operating under different engine loads, rotation speeds and valve clearance settings. The parameterization pertained to the specific features of the vibration signals, the derivative of the vibration signal as a function of time as well as the envelope of this derivative. In the first approach, the authors developed a classifier in the form of a set of binary trees that additionally allowed distinguishing the features significant in terms of the identification of adopted classes. For comparison, the authors also developed classifiers in the form of a neural network as well as a k-nearest neighbors algorithm using the Euclidean metric. Based on the performed investigations and analyses a method of valve clearance assessment has been proposed.
PL
Dynamiczny rozwój konstrukcji silników spalinowych generuje potrzebę wprowadzenia strategii eksploatacji jednostek napędowych, opartej na znajomości ich stanu technicznego. W artykule poddano analizie zagadnienia, związane z drganiową diagnostyką luzu zaworów tłokowego silnika spalinowego, istotnego ze względu na efektywność pracy silnika i jego trwałość. Zaproponowano wykorzystanie metod klasyfikacji do oceny poprawności luzu zaworowego. Przeprowadzono i opisano eksperymenty, które miały na celu dostarczenie informacji koniecznych do zbudowania i zweryfikowania zaproponowanych metod. W przeprowadzonych badaniach pozyskano sygnały drganiowe z trójosiowego czujnika przyspieszeń drgań zlokalizowanego na głowicy silnika. Dokonano parametryzacji uzyskanych przebiegów czasowych sygnału drganiowego dla silnika pracującego pod różnym obciążeniem, z różnymi prędkościami obrotowymi oraz z różnymi luzami zaworowymi. Parametryzacja dotyczyła zarówno cech sygnału przyspieszeń drgań, pochodnej przyspieszeń drgań względem czasu jak i obwiedni tej pochodnej. W pierwszym podejściu zbudowano klasyfikator w postaci zbioru drzew binarnych, który przy okazji pozwolił na wyodrębnienie istotnych, ze względu na przyjęte klasy, cech. Dla porównania zbudowano także klasyfikatory w postaci sieci neuronowej jak i algorytmu k – najbliższych sąsiadów z metryką euklidesową. Na podstawie przeprowadzonych badań i analiz zaproponowano metodę oceny luzu zaworowego.
Rocznik
Strony
331--339
Opis fizyczny
Bibliogr. 35 poz., rys., tab.
Twórcy
Bibliografia
  • 1. Albarbar A, Ball A, Starr A. On acoustic measurement-based condition monitoring of internal combustion engines. Insight: Non-Destructive Testing and Condition Monitoring 2008; 50(1): 30-34, https://doi.org/10.1784/insi.2008.50.1.30.
  • 2. Albertson F, Bodén H, Gilbert J. Comparison of different methods to couple nonlinear source descriptions in the time domain to linear system descriptions in the frequency domain - Application to a simple valveless one-cylinder cold engine. Journal of Sound and Vibration 2006; 291(3-5): 963-985, https://doi.org/10.1016/j.jsv.2005.07.046.
  • 3. Arroyo J, Muñoz M, Moreno F, Bernal N, et al. Diagnostic method based on the analysis of the vibration and acoustic emission energy for emergency diesel generators in nuclear plants. Applied Acoustics 2013; 74(4): 502-508, https://doi.org/10.1016/j.apacoust.2012.09.010.
  • 4. Babu A K, Raj A A, Kumersan G. Misfire detection a multi- cylinder diesel engine: a machune learning approach. Journal of Engineering Science and Technology 2016; 11(2): 278-295.
  • 5. Badawy T, Shrestha A, Henein N. Detection of Combustion Resonance Using an Ion Current Sensor in Diesel Engines. Journal of Engineering for Gas Turbines and Power 2012; 134(5): 052-802, https://doi.org/10.1115/1.4004840.
  • 6. Ahmed A B, Elaraby I S. A prediction for Student's Performance Using Decision Tree ID3 Method. India - World Journal of Computer Application and Technology 2014; 2(2): 43-47.
  • 7. Czech P, Ba̧kowski H. Diagnosing of car engine fuel injectors damage using dwt analysis and PNN neural networks. Transport Problems 2013; 8(3): 85-91.
  • 8. Delvecchio S, Bonfiglio P, Pompoli F. Vibro-acoustic condition monitoring of Internal Combustion Engines: A critical review of existing techniques. Mechanical Systems and Signal Processing 2018; 99: 661-683, https://doi.org/10.1016/j.ymssp.2017.06.033.
  • 9. Desbazeille M, Randall R B, Guillet F, El Badaoui M, et al. Model-based diagnosis of large diesel engines based on angular speed variations of the crankshaft. Mechanical Systems and Signal Processing 2010; 24(5): 1529-1541, https://doi.org/10.1016/j.ymssp.2009.12.004.
  • 10. Dolatabadi N., Theodossiades S., Rothberg S.J., On the identification of piston slap events in internal combustion engines using tribodynamic analysis. Mechanical Systems and Signal Processing 2015; 58: 308-324, https://doi.org/10.1016/j.ymssp.2014.11.012.
  • 11. Erbek F S, Özkan C, Taberner M. Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. International Journal of Remote Sensing 2004; 25(9): 1733-1748, https://doi.org/10.1080/0143116031000150077.
  • 12. Figlus T, Liščák Š, Wilk A, Łazarz B. Condition monitoring of engine timing system by using wavelet packet decomposition of a acoustic signal. Journal of Mechanical Science and Technology 2014; 28(5): 1663-1671, https://doi.org/10.1007/s12206-014-0311-3.
  • 13. Fog TL, Hansen LK, Larsen J, Hansen HS, et al. On condition monitoring of exhaust valves in marine diesel engines. Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop 1999; 554-563.
  • 14. Gao F, Lv J. Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine. Mathematical Problems in Engineering 2016; 2016: 1-10, https://doi.org/10.1155/2016/7939607.
  • 15. Gorges C, Öztürk K, Liebich R. Impact detection using a machine learning approach and experimental road roughness classification. Mechanical Systems and Signal Processing 2019; 117: 738-756, https://doi.org/10.1016/j.ymssp.2018.07.043.
  • 16. Jia F, Lei Y, Lu N, Xing S. Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization. Mechanical Systems and Signal Processing 2018; 110: 349-367, https://doi.org/10.1016/j.ymssp.2018.03.025.
  • 17. Korbicz J, Kościelny J M, Kowalczuk Z, Cholewa W (Eds.). Models, Artificial Intelligence, Applications. Springer-Verlag Berlin Heidelberg, 2004.
  • 18. Lahrache A, Cocconcelli M, Rubini R. Anomaly detection in a cutting tool by k-means clustering and support vector machines. Diagnostyka 2017; 18(3): 21-29.
  • 19. Leemans V, Magein H, Destain M F. Defect segmentation on 'Jonagold' apples using colour vision and a Bayesian classification method. Computers and Electronics in Agriculture 1999; 23: 43-53, https://doi.org/10.1016/S0168-1699(99)00006-X.
  • 20. Lei Y, He Z, Zi Y. Application of an intelligent classification method to mechanical fault diagnosis. Expert Systems with Applications 2009; 36(6): 9941-9948, https://doi.org/10.1016/j.eswa.2009.01.065.
  • 21. Madej H, Czech P. Discrete wavelet transform and probabilistic neural network in IC engine fault diagnosis. Eksplotacja i Niezawodnosc - Maintenance and reliability 2010; 4(48): 47-54.
  • 22. Matzke W. Four-stroke engine timing, Warszawa: WKiŁ, 1967.
  • 23. Mechefske C K, Mathew J. Fault detection and diagnosis in low speed rolling element bearings Part II: The use of nearest neighbour classification. Mechanical Systems and Signal Processing 1992; 6(4): 309-316, https://doi.org/10.1016/0888-3270(92)90033-F.
  • 24. Nguyen T T, Armitage G. A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys Tutorials 2008, 10(4): 56-76, https://doi.org/10.1109/SURV.2008.080406.
  • 25. Niziński S, Michalski R. Diagnostics of technical objects. Radom: Department of Vehicle and Machine Operation, Faculty of Technical Sciences, University of Warmia and Mazury in Olsztyn, Institute for Sustainable Technologies in Radom, 2002.
  • 26. Osiecki J, Ziemba S. Basics of mechanical vibration measurements, Warszawa: PWN, 1968.
  • 27. Potočnik P, Govekar E. Semi-supervised vibration-based classification and condition monitoring of compressors. Mechanical Systems and Signal Processing 2017; 93: 51-65, https://doi.org/10.1016/j.ymssp.2017.01.048.
  • 28. Qinghua W, Youyun Z, Lei C, Yongsheng Z. Fault diagnosis for diesel valve trains based on non-negative matrix factorization and neural network ensemble. Mechanical Systems and Signal Processing 2009; 23(5): 1683-1695, https://doi.org/10.1016/j.ymssp.2008.12.004.
  • 29. Ruiz M, Mujica L E, Alférez S, Acho L, et al. Wind turbine fault detection and classification by means of image texture analysis. Mechanical Systems and Signal Processing 2018; 107: 149-167, https://doi.org/10.1016/j.ymssp.2017.12.035.
  • 30. Serridge M, Licht T R. Piezoelectric accelerometers and vibration preamplifiers, Nearum: Brüel & Kjær, 1987.
  • 31. Szymański G.M., Tomaszewski F., Diagnostics of automatic compensators of valve clearance in combustion engine with the use of vibration signal. Mechanical Systems and Signal Processing 2016; 68-69: 479-490, https://doi.org/10.1016/j.ymssp.2015.07.015.
  • 32. Wang C, Zhang Y, Zhong Z. Fault diagnosis for diesel valve trains based on time-frequency images. Mechanical Systems and Signal Processing 2008; 22(8), 1981-1993, https://doi.org/10.1016/j.ymssp.2008.01.016.
  • 33. Wong P K, Zhong J, Yang Z, Vong C M. Sparse Bayesian extreme learning committee machine for engine simultaneous fault diagnosis. Neurocomputing 2016; 174: 331-343, https://doi.org/10.1016/j.neucom.2015.02.097.
  • 34. Wu J, Liu C. An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network. Expert Systems with Applications 2009, 36: 4278-4286, https://doi.org/10.1016/j.eswa.2008.03.008.
  • 35. Zhang H, Li J, Huang Y, Zhang L. A Nonlocal Weighted Joint Sparse Representation Classification Method for Hyperspectral Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2014; 7(6): 2056-2065, https://doi.org/10.1109/JSTARS.2013.2264720.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1f586722-dce1-4244-a6e0-f721a9d5d20c
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