Dual-Tree Complex Wavelet Transform and SVD-Based Acceleration Signals Denoising and its Application in Fault Features Enhancement for Wind Turbine

Abstract

Since strong background noise is inevitably embedded in non-stationary signals collected under faulty condition, and the difficulty to obtain fault characteristic frequencies in practice, noise reduction should be paid more attention to extract fault features. A novel fault feature- extracted method is put forward by means of dual-tree complex wavelet transform (DTCWT) and singular value decomposition (SVD). Dual-tree complex wavelet transform is considered as the key technique to perform multilevel decomposition and several different frequency band components are obtained. A Hankel matrix is constructed by employing the components which contain the fault information, and the singular values are obtained after the singular value decomposition. According to principal component analysis, the number of singular values is determined to realize noise reduction using SVD reconstruction. Finally, the fault frequency can be identified accurately by Hilbert envelop spectrum. The results of the experiments and practical engineering applications demonstrate that the fault feature of wind turbine can be extracted effectively. The integrated method of DTCWT and SVD is adopted to reduce noise, which can provide some guidance for theoretical research and engineering applications in fault diagnosis.