Abstract: The actual denoising effect of wavelet denoising methed was studied through pipe flow test apparatus, which simulated the actual pipeline leakage condition. The reason why small leak was not easy to be found was explained from two expects through timefrequency analysis of the leakage signal attenuation process. And the disadvantages of the most commonly used wavelet de-noising method to deal with small leakage signal were also discussed. On this basis, a new threshold function was established to improve the signal reconstruction accuracy and the advantage was analyzed mathematically. Next, a blind source separation algorithm based on maximum signal-to-noise ratio (SNR) was proposed, which integrated wavelet transform with blind source separation. By separating the known structural signal, the applicability of this method was illustrated, and the practical denoising effect and industrial application value of the fusion algorithm were verified.

Key words: Time-frequency analysis, Wavelet denoising, New threshold function, Blind source separation, Fusion algorithm

摘要： 利用环道实验模拟实际管道泄漏工况，验证了小波去噪的实际效果，通过时频联合分析泄漏信号的衰减过程，从两方面阐述了小泄漏不易被发现的原因，并且讨论了现阶段最常用的小波去噪方法处理小泄漏信号的不足。针对上述问题，建立新阈值函数提高小波去噪方法的信号重构精度，并从数学角度分析了新函数的优越性，提出了基于最大信噪比的盲源分离算法，将小波变换与盲源分离相融合，通过分离已知构造信号说明此方法的适用性，验证该融合算法的实际去噪效果和工业应用价值。

关键词: 时频联合, 小波去噪, 新阈值函数, 盲源分离, 融合算法