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Nonlinear Blind Source Separation of Multi-Sensor signals for Marine Diesel Engine Fault Diagnosis

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Warianty tytułu
PL
Wykorzystanie nieliniowej ślepej separacja sygnałów wielu czujników do diagnostyki silnika Diesla w napędach okrętowych
Języki publikacji
EN
Abstrakty
EN
Marine diesel engines are the heart of the ships. They provide the power for the normal propulsion of the vessels. Any unexpected failures occurred in the marine diesel engines may lead to terrible accident. It is therefore imperative to monitor the marine diesel engines to prevent impending faults. In the present work, a new defect detection method for the marine diesel engines using the artificial intelligence has been proposed. The vibration signals of the marine diesel engine were recorded by the multi-channel sensors. The nonlinear independent component analysis (NICA) was adopted as the data fusion approach to find the characteristic vibration signals of the marine diesel engine fault from the multiply sensor collections. Then the Empirical Mode Decomposition (EMD) was employed to extract the feature vector of the fused vibration signals. Lastly, the Genetic Algorithm-Chaos and RBF neural network was used to recognize the fault patterns of the marine diesel engine. The experimental tests were implemented in a real ship to evaluate the effectiveness of the proposed diagnosis approach. The diagnosis results have showed that distinguished fault features have been extracted and the fault identification accuracy is satisfactory. In addition, the classification rate of the proposed method is superior to the traditional linear ICA based methods.
PL
Wykorzystano nieliniową niezależną analizę składników NICA do diagnostyki wibracji silnika Diesla. Zastosowano metodę empirycznej dekompozycji EMD do separacji sygnałów. Następnie wykorzystano sieci neuronowe i algorytm genetyczny do identyfikacji uszkodzeń.
Rocznik
Strony
271--274
Opis fizyczny
Bibliogr. 18 poz., rys.
Twórcy
autor
  • Key Laboratory of High Performance Ship Technology of Ministry of Education (Wuhan University of Technology), Wuhan 430063, China
  • Reliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China
autor
  • Key Laboratory of High Performance Ship Technology of Ministry of Education (Wuhan University of Technology), Wuhan 430063, China
  • Reliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China
autor
  • Key Laboratory of High Performance Ship Technology of Ministry of Education (Wuhan University of Technology), Wuhan 430063, China
Bibliografia
  • [1] Grega W., Information technologies supporting control and monitoring of power systems, PRZEGLAD ELEKTROTECHNICZNY, 88 (2012), No. 5A, 193-197
  • [2] Li Z., Yan X., Yuan C., Peng Z., Li L., Virtual prototype and experimental research gear multi — fault diagnosis using wavelet — autoregressive model and principal component analysis method, Mechanical Systems and Signal Processing, 25 (2011), 2589-2607
  • [3] Cao C., Yang S., Yang J., A new fault diagnosis method for a rotor of a steam turbine generator set based on instantaneous energy distribution characteristics, Journal of Vibration and Shock, 28 (2009), 35-39
  • [4] Wierzbicki R., Kowalski T., Stator and Rotor Faults Detection in Direct Field Oriented Closed Loop Induction Motor Drive, PRZEGLAD ELEKTROTECHNICZNY, 88 (2012), No. 4B, 265-269
  • [5] Li Z., Yan X., Independent component analysis and manifold learning with applications to fault diagnosis of VSC-HVDC systems, Hsi-An Chiao Tung Ta Hsueh, 45 (2011), 44-48
  • [6] Li Z., Yan X., Guo Z., Liu P., Yuan C., Peng Z., A new intelligent fusion method of multi-dimensional sensors and its application to tribo-system fault diagnosis of marine diesel engines, Tribology Letters, 47 (2012), 1-15
  • [7] McFadden P., Examination of a technique for the early detection of failure in gears by signal processing of the time domain average of the meshing vibration, Mechanical Systems and Signal Processing, 1 (1987), 173-183
  • [8] Giannakis G., Swami A., New results on state-space and input-output identification of non-Gaussian processing using cumulants, Proc SPIE'87. San Diego: [S. N.], 1987: 199–205.
  • [9] Jutten C., Herault J., Blind separation of sources, part Ⅰ: an adaptive algorithm based on neuromimatic architecture, Signal Processing, 24 (1991), 1–10
  • [10] Comon P., Blind separation of sources, partⅡ: problem statement , Signal Processing, 24 (1991), 11–20
  • [11] Sorouchyari E., Blind separation of sources, part Ⅲ: stability analysis, Signal Processing, 24 (1991), 21–29
  • [12] Deco G., Brauer W., Nonlinear higher-order statistical decorrelation by volume-conserving neural architectures, Neural Networks, 8 (1995), 525–535
  • [13] Pajunen P., Hyvarinen A., Karhunen J., Nonliner blind source separation by self-organizing maps, in Progress in Neural Information Processing: Proc. ICONIP’96, New York, 2 (1996), 1207–1210
  • [14] Burel G., Blind separation of sources: A nonlinear neural algorithm, Neural Networks, 5 (1992), 937–947
  • [15] Taleb A., Jutten C., Olympieff S., Source separation in post nonlinear mixtures: An entropy-based algorithm, in Proc. ESANN’98, 1998, 2089–2092
  • [16] Tan Y., Wang J., Zuradam M., Nonlinear blind source separation using a radial basis function network, IEEE Transactions on Neural Network, 12 (2001), 124–134
  • [17] Huang N., Shen Z., S. Long, et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis, Proc. R. Soc. Lond. A, 454 (1998), 903-905
  • [18] Cheng M., Huang K., Genetic algorithm-based chaos clustering approach for nonlinear optimization Journal of Marine Science and Technology, 18 (2010), 435-441
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9c2621f9-2345-4df8-86a1-01e2dd6d3d04
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