Wyniki wyszukiwania - Biblioteka Nauki

1

Deep learning-based fault diagnosis for marine centrifugal fan

100%

Li C. , Hu Y. , Jiang J. , Yan G.

Polish Maritime Research

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2023

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tom nr 1

112--120

EN

Marine centrifugal fans usually work in harsh environments. Their vibration signals are non-linear. The traditional fault diagnosis methods of fans require much calculation and have low operating efficiency. Only shallow fault features can be extracted. As a result, the diagnosis accuracy is not high. It is difficult to realize the end-to-end fault diagnosis. Combining the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and lightweight neural network, a fault classification method is proposed. First, the CEEMDAN can decompose the vibration signal into several intrinsic modal functions (IMF). Then, the original signals can be transformed into 2-D images through pseudocolour coding of the IMFs. Finally, they are fed into the lightweight neural network for fault diagnosis. By embedding a convolutional block attention module (CBAM), the ability of the network to extract critical feature information is improved. The results show that the proposed method can adaptively extract the fault characteristics of a marine centrifugal fan. While the model is lightweight, the overall diagnostic accuracy can reach 99.3%. As exploratory basic research, this method can provide a reference for intelligent fault diagnosis systems on ships.

2

Load identification method of ball mill based on the CEEMDAN-wavelet threshold - PMMFE

71%

Yang L. , Yang H.

Gospodarka Surowcami Mineralnymi

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2024

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tom T. 40, z. 2

163--180

EN

In order to address the difficult problem of ball mill load identification during milling operation, the multi-scale fuzzy entropy algorithm is introduced into ball mill load identification and an innovative ball mill load identification method is proposed - the complete integrated empirical decomposition based on adaptive noise (CEEMDAN)-joint denoising with wavelet thresholding-multi-scale fuzzy entropy biased mean value (PMMFE) ball mill load identification method. Firstly, the vibration signals of ball mill bearings are denoised by the CEEMDAN-wavelet threshold joint denoising method and the analysis reveals that this method has obvious advantages over other denoising methods; secondly, the fuzzy entropy, multi-scale fuzzy entropy, and multi-scale fuzzy entropy deviation of denoised vibration signals are computed, the relationship between each entropy feature and the mill load is analysed in-depth and in an information-rich manner. Finally, the least squares support vector algorithm is used to identify the load of the feature vector. The analysis of the measured vibration signals reveals that the overall recognition rate of this method is 84.4%, which is significantly higher than that of other denoising methods and the combination of feature parameters, and the experiments show that the mill load recognition method based on CEEMDAN-wavelet thresholding-PMMFE is able to effectively identify the different loading states of ball mills.

PL

W celu rozwiązania trudnego problemu identyfikacji obciążenia młyna kulowego podczas operacji mielenia, do identyfikacji obciążenia młyna kulowego wprowadzono wieloskalowy algorytm entropii rozmytej oraz zaproponowano innowacyjną metodę identyfikacji obciążenia młyna kulowego – pełną zintegrowaną dekompozycję empiryczną opartą na szumie adaptacyjnym (CEEMDAN) – wspólne odszumianie z progowaniem falkowym – wieloskalowa metoda identyfikacji obciążenia młyna kulowego metodą rozmytej entropii z odchyleniem wartości średniej (PMMFE). Po pierwsze, sygnały wibracyjne łożysk młyna kulowego są odszumiane za pomocą wspólnej metody odszumiania CEEMDAN z progowaniem falkowym, a analiza pokazuje, że metoda ta ma oczywiste zalety w porównaniu z innymi metodami odszumiania; po drugie, obliczana jest rozmyta entropia, wieloskalowa rozmyta entropia i wieloskalowe rozmyte odchylenie entropii odszumionych sygnałów wibracyjnych, a związek między każdą cechą entropii a obciążeniem młyna jest analizowany dogłębnie i w sposób bogaty w informacje. Na koniec, algorytm wektora wsparcia najmniejszych kwadratów jest wykorzystywany do identyfikacji obciążenia wektora cech. Analiza zmierzonych sygnałów wibracyjnych pokazuje, że ogólny wskaźnik rozpoznawania tej metody wynosi 84,4%, co jest znacznie wyższe niż w przypadku innych metod odszumiania i kombinacji parametrów cech, a eksperymenty pokazują, że metoda rozpoznawania obciążenia młyna oparta na progowaniu falkowym CEEMDAN-PMMFE jest w stanie skutecznie identyfikować różne stany obciążenia młynów kulowych.

3

A combined iCEEMDAN and VMD method for mitigating the impact of ionospheric scintillation on GNSS signals

71%

Dey A. , Chhibba R. , Ratnam D. V. , Sharma N.

Acta Geophysica

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2021

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tom Vol. 69, no. 5

1933--1948

EN

Severe amplitude and phase scintillation induced by the ionospheric plasma density irregularities degrades the performance of global navigation satellite system (GNSS) receivers. Scintillation typically has adverse effects at the tracking process and thus adversely affects the raw GNSS measurements used in a number of applications. Hence, it is important to develop robust methodologies for detecting and mitigating ionospheric effects on the GNSS signals. In this paper, we propose a novel method based on the combination of improved complete ensemble empirical mode decomposition with adaptive noise (iCEEMDAN) and variational mode decomposition (VMD) methods. The proposed method employs a detrended fuctuation analysis (DFA)-based metric for robust thresholding between the scintillation-free and amplitude scintillated GNSS signals. The major contribution of the proposed method is development of novel approaches for selection of intrinsic mode functions (IMFs) based on DFA and optimised selection of [K, 훼] parameters of the VMD. The performance of the proposed method was evaluated and was observed that it is better than existing ionospheric scintillation effects mitigation algorithms for both simulated and real-time GPS scintillation datasets. The proposed method can denoise approximately 9.23–15.30 dB scintillation noise from the synthetic and 0.2–0.48 from the real scintillation index (S4) values. Therefore, the proposed (iCEEMDAN-VMD) method is appropriate for mitigating the ionospheric scintillation effects on the GNSS signals.