Enhanced Characteristic Vibration Signal Detection of Generator Based on Time-Wavelet Energy Spectrum and Multipoint Optimal Minimum Entropy Deconvolution Adjusted Method. (20th April 2020)

• Record Type: Journal Article
• Authors: He, Yu-Ling; Wang, Tao; Sun, Kai; Wang, Xiao-Long; Peng, Bo; Wan, Shu-Ting
• Other Names: Renno Carlo Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2020(2020)
• Publication Date: 2020-04-20
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2020/6916289
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : To overcome the shortage of low SNR (signal to noise ratio) of the multipole generator vibration signal which brings rigid difficulty to the fault diagnosis, a new method which combines the Time-Wavelet Energy Spectrum (TWES) with the Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) algorithm is proposed. This method uses TWES to extract and enhance the characteristic signal, while employing MOMEDA to optimize the spectrum structure and filter the noise. The application of this method to the simulating signal as well as the test stator vibration signal in a 6-pole generator before and after rotor interturn short circuit fault validates the effectiveness of the method. Moreover, the comparison among the proposed method and some other general methods such as the Empirical Mode Decomposition (EMD) and the maximum correlative kurtosis deconvolution (MCKD) suggests that the proposed method is superior to these methods.