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

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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.

Key words： Stationary wavelet packet transform(SWPT) Hilbert transform(HT) adaptive signal analysis multiresolution spectrum analysis fault detection

Received: 31 March 2008 Published: 11 February 2014

PACS:

TM711

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	Liu Yihua
	Wang Yuanyuan
	Song Zhihuan

Cite this article:

Liu Yihua,Wang Yuanyuan,Song Zhihuan. Adaptive Fault Feature Extraction Based on Stationary Wavelet Packet Decomposition and Hilbert Transform[J]. Transactions of China Electrotechnical Society, 2009, 24(2): 144-149.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2009/V24/I2/144

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