Wyniki wyszukiwania - BazTech

1

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

Chenxing S., Zhe T., Yuelei Z.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 1b

271--274

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.

2

Online Blind Separation of Dependent Sources Using Nonnegative Matrix Factorization Based on KL Divergence

Li H., Shen Y., Wang J.

Przegląd Elektrotechniczny

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2012

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R. 88, nr 1b

278-281

EN

This paper proposes a novel online algorithm for nonnegative matrix factorization (NMF) based on the generalized Kullback-Leibler (KL) divergence criterion, aimed to overcome the high computation problem of large-scale data brought about by conventional batch NMF algorithms. It features stable updating the factors alternately for each new-coming observation, and provides an efficient solution for the blind separation of statistically dependent sources (i.e., the sources are mutually correlated). Our theoretic analysis is validated by simulation examples.

PL

Przedstawiono nowy algorytm do faktoryzacji nieujemnej macierzy bazujący na kryterium Kullback-Leibler, pozwalający usprawnić problem obliczeń dużej ilości danych. Algorytm sukcesywnie zmienia współczynniki i pozwala na ślepą separację statystycznie zależnych źródeł.

3

Multidimensional independent subspace analysis by natural gradient

Wang F., Zhang L., Li R., Zhou Y., Liu N.

Przegląd Elektrotechniczny

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2012

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R. 88, nr 9b

51-54

EN

Multidimensional Independent Subspace Analysis (MISA) as an extended Independent Component Analysis (ICA) method has been considered. The general and detailed definition, existence, uniqueness, separability of the MISA model are given and the relationships between ICA and MISA are also discussed. The natural gradient separation algorithm and corresponding simulation results for MISA are constructed based on the maximum likelihood theory and natural gradient method.

PL

W artykule zaprezentowano metodę MISA – multidimensional independent subspace analysis. Przedstawiono też metode IOCA – independent component analysis. Opracowano algorytm separacji – natural gradient separation algorithm.

4

Underdetermined Blind Separation using modified subspace-based algorithm in the time-frequency domain

Li H., Shen Y-H., Cao M., Wang J-G.

Przegląd Elektrotechniczny

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2011

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R. 87, nr 7

280-283

EN

To solve the underdetermined blind separation (UBSS) problem, Aissa-El-Bey et al. have proposed the significant subspace-based algorithms in the time-frequency (TF) domain, where a fixed (maximum) value of K, i.e., the number of active sources overlapping at any TF point, is considered for simplicity. In this paper, based on the principle component analysis (PCA) technology, we propose a modified algorithm by estimating the number K for selected frequency bins where most energy is concentrated. Improved performances are obtained without increasing complexity.

PL

Do rozwiązania problem nieokreślonej ślepej separacji (UBSS) Aissa-El_Bey zaproponował algorytm czasowo-częstotliwościowy gdzie ustalono liczbę aktywnych źródeł pokrywających każdy punkt TF. W artykule zaproponowano zmodyfikowany algorytm bazujący na analizie składowej głównej PCA. Otrzymano poprawę parametrów bez powiększania skomplikowania metody.