- Tytuł:
- Wavelets and principal component analysis method for vibration monitoring of rotating machinery
- Autorzy:
-
Bendjama, H.
Boucherit, M. S. - Powiązania:
- https://bibliotekanauki.pl/articles/949212.pdf
- Data publikacji:
- 2016
- Wydawca:
- Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
- Tematy:
-
vibration
fault diagnosis
wavelet analysis
principal component analysis, squared
prediction error - Opis:
- Fault diagnosis is playing today a crucial role in industrial systems. To improve reliability, safety and efficiency advanced monitoring methods have become increasingly important for many systems. The vibration analysis method is essential in improving condition monitoring and fault diagnosis of rotating machinery. Effective utilization of vibration signals depends upon effectiveness of applied signal processing techniques. In this paper, fault diagnosis is performed using a combination between Wavelet Transform (WT) and Principal Component Analysis (PCA). The WT is employed to decompose the vibration signal of measurements data in different frequency bands. The obtained decomposition levels are used as the input to the PCA method for fault identification using, respectively, the Q-statistic, also called Squared Prediction Error (SPE) and the Q-contribution. Clearly, useful information about the fault can be contained in some levels of wavelet decomposition. For this purpose, the Q-contribution is used as an evaluation criterion to select the optimal level, which contains the maximum information.Associated to spectral analysis and envelope analysis, it allows clear visualization of fault frequencies. The objective of this method is to obtain the information contained in the measured data. The monitoring results using real sensor measurements from a pilot scale are presented and discussed.
- Źródło:
-
Journal of Theoretical and Applied Mechanics; 2016, 54, 2; 659-670
1429-2955 - Pojawia się w:
- Journal of Theoretical and Applied Mechanics
- Dostawca treści:
- Biblioteka Nauki
Artykuł