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Can I read Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution on EtoBox?
Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution by Baoping Tang; Wenyi Liu; Tao Song is a Engineering article available to read on EtoBox.
What is Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution about?
Based on the Morlet wavelet transformation and Wigner-Ville distribution (WVD), we present a wind turbine fault diagnosis method in this paper. Wind turbine can be damaged by moisture absorption, fatigue, wind gusts or lightening strikes. Due to this reason, there is an increasing need to monitor the health of these structures. Vibration analysis is the best-known technology applied in wind turbine condition monitoring, in which the time-frequency analysis techniques such as Wigner-Ville distribution (WVD) are widely used. Theoretically WVD has an infinite resolution in time-frequency domain. For early wind turbine fault signals, however, there are two main difficulties in WVD analysis. One is strong noise signals in the background and the other is cross terms in WVD itself. In this paper, continuous wavelet transformation (CWT) is employed to filter useless noise in raw vibration signals, and auto terms window (ATW) function is used to suppress the cross terms in WVD. In the CWT de-noising process, the Morlet wavelet, whose shape is similar to mechanical shock signals, is chosen to perform CWT on the raw vibration signals. The appropriate scale parameter for CWT is optimized by th
Who reads Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Baoping Tang; Wenyi Liu; Tao Song
- Publisher
- Elsevier BV
- Published
- 2010
- Language
- EN
- Field
- Engineering (Physical Sciences)