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Wyświetlanie 1-2 z 2
Tytuł:
A new method for detection of rolling bearing faults based on the Local Curve Roughness approach
Autorzy:
Behzad, M.
Bastami, A. R.
Powiązania:
https://bibliotekanauki.pl/articles/258560.pdf
Data publikacji:
2011
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
rolling bearing
diagnosis
vibration
local curve roughness
Opis:
Detection of rolling bearing faults by vibration analysis is an important part of condition monitoring programs. In this paper a new method for detection of bearing defects based on a new concept of local surface roughness, is proposed. When a defect in the bearing grows then roughness of the defective surface increases and measurement of the roughness can be a good indicator of the bearing defect. In this paper a method of indirectly measuring surface roughness by using vibration signal is introduced. Several attached examples including both numerically simulated signals and actual experimental data show the effectiveness of the new, easy-to-implement method.
Źródło:
Polish Maritime Research; 2011, 2; 44-50
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel fault diagnosis method for marine blower with vibration signals
Autorzy:
Yan, Guohua
Hu, Yihuai
Jiang, Jiawei
Powiązania:
https://bibliotekanauki.pl/articles/32899234.pdf
Data publikacji:
2022
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
fault diagnosis
marine blower
EEMD
correlation coefficient
AR spectrum
BPNN
Opis:
The vibration signals on marine blowers are non-linear and non-stationary. In addition, the equipment in marine engine room is numerous and affects each other, which makes it difficult to extract fault features of vibration signals in the time domain. This paper proposes a fault diagnosis method based on the combination of Ensemble Empirical Mode Decomposition (EEMD), an Autoregressive model (AR model) and the correlation coefficient method. Firstly, a series of Intrinsic Mode Function (IMF) components were obtained after the vibration signal was decomposed by EEMD. Secondly, effective IMF components were selected by the correlation coefficient method. AR models were established and the power spectrum was analysed. It was verified that blower failure can be accurately diagnosed. In addition, an intelligent diagnosis method was proposed based on the combination of EEMD energy and a Back Propagation Neural Network (BPNN), with a correlation coefficient method to get effective IMF components, and the energy components were calculated, normalised as a feature vector. Finally, the feature vector was sent to the BPNN for training and state recognition. The results indicated that the EEMD-BPNN intelligent fault diagnosis method is suitable for higly accurate fault diagnosis of marine blowers.
Źródło:
Polish Maritime Research; 2022, 2; 77-86
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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