Abstract:A fault detection algorithm is proposed to adaptively extract the characteristic component of fault signals in this paper. The algorithm used one-level stationary wavelet packet transform to decompose the signal into low-and high-frequency subbands, whose instantaneous frequency and instantaneous amplitude are calculated by Hilbert transform at the same time. Based on the preset rules of component extraction and signal decomposition, the algorithm adaptively selects the path of stationary wavelet packet decomposition, building the wavelet packet decomposition tree of the signal with a multiresolution spectral analysis. Thus the algorithm denoiseds the signal and extracts the characteristic components for fault detection. The simulation show that the algorithm provides sufficient frequency-amplitude fault information with the less computational workloads and data storage spaces. The algorithm also has a good anti-noise performance.
刘毅华, 王媛媛, 宋执环. 基于平稳小波包分解和希尔伯特变换的故障特征自适应提取[J]. 电工技术学报, 2009, 24(2): 144-149.
Liu Yihua, Wang Yuanyuan, Song Zhihuan. Adaptive Fault Feature Extraction Based on Stationary Wavelet Packet Decomposition and Hilbert Transform. Transactions of China Electrotechnical Society, 2009, 24(2): 144-149.
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