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中文摘要：
声发射信号经常淹没在各种复杂的非平稳噪声中，使得对有用的声发射的识别非常困难，因此，在声发射识别时进行信号增强成为诸多研究者的关注热点。离散余弦变换是声信号增强的有效方法。基于态函数，给出了分数余弦变换的新定义，提出三周期离散分数余弦变换方法，介绍了基于三周期离散分数余弦变换的声发射信号增强算法和改进算法的推导过程。实验数据为在转子实验台上采集的碰摩声发射信号，通过在该信号上叠加高斯白噪声和非平稳噪声来获得模拟的严重噪声污染的声发射信号，然后用增强算法及改进算法对该信号进行降噪处理和声发射信号识别。实验结果表明：两种算法对多种非平稳噪声环境下的碰摩声发射信号均能取得较好的降噪效果，且优于离散余弦变换，是声发射信号识别的有效途径。
英文摘要：
A number of non-stationary noises usually cover up the useful acoustic emission （AE） signals in real world, which induce the difficulty in AE recognition. So, a number of recent studies have focused on the signal enhancement during AE recognition. Discrete cosine transform （DCT） is an effective method to signal enhancement. This paper redefined discrete fractional cosine transform （DFCT） by state function, proposed a algorithm named three cycles discrete fractional cosine transform （DFCT3）, deduced the AE enhancement method based on DFCT3 and its modified algorithm （MDFCT3）. The experiment date was rub impact AE signals sampled from rotating test stand, Gaussian white noise and non-stationary noise were added to simulate the real AE signal which polluted seriously by noise. Then, DFCT3 and MDFCT3 were used into de-noising and AE recognition. The results show that these two algorithms have better performance than DCT in multi-noise circumstance, and that the methods in this paper are effective.
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