本文選題：多源分離 + 小波包分析　； 參考：《上海交通大學(xué)學(xué)報(bào)》2016年05期

【摘要】：針對(duì)旋轉(zhuǎn)機(jī)械設(shè)備中同時(shí)存在的裂紋、摩擦等多故障源信號(hào)難以檢測(cè)和分離的問(wèn)題,提出了一種基于小波包分析(WPA)與獨(dú)立分量分析(ICA)的多源故障信號(hào)提取方法,即首先用WPA對(duì)含噪線性混合信號(hào)降噪預(yù)處理,由db2小波基函數(shù)進(jìn)行5層分解后保留62.5~187.5kHz頻段信號(hào),然后采用ICA中的FastICA算法對(duì)降噪后的混合信號(hào)分離,最后對(duì)各通道分離出的信號(hào)用收縮函數(shù)進(jìn)行頻段內(nèi)去噪處理.對(duì)不同輸入信噪比的含噪微弱裂紋和摩擦信號(hào)進(jìn)行提取和分析的結(jié)果表明,該方法能有效提取出輸入信噪比大于-15dB的裂紋和摩擦信號(hào).當(dāng)混合信號(hào)信噪比為-15dB時(shí),裂紋和摩擦信號(hào)的輸出信噪比分別為-1.31和-1.36dB,相關(guān)系數(shù)分別為0.62和0.63,提取效果好于結(jié)合小波包和FastICA分離方法(信噪比分別為-1.74和-2.06dB,相關(guān)系數(shù)分別為0.59和0.59)以及單獨(dú)采用FastICA算法(信噪比分別為-4.57和-4.31dB,相關(guān)系數(shù)分別為0.17和0.19).因此,所提出的綜合WPA和ICA的方法是一種較好的多源微弱信號(hào)提取方法.
[Abstract]:In order to solve the problem that it is difficult to detect and separate the signals of multi-fault sources such as cracks and friction in rotating machinery, a multi-source fault signal extraction method based on wavelet packet analysis (WPA) and independent component analysis (ICA) is proposed. Firstly, the noise reduction of the noisy linear mixed signal is pretreated, the db2 wavelet basis function is decomposed into five layers and the 62.5 ~ 187.5 kHz band signal is retained, then the noise reduction mixed signal is separated by the fast ICA algorithm. Finally, the signal separated from each channel is de-noised by shrinkage function in frequency band. The results of extraction and analysis of weak cracks and frictional signals with different input signal-to-noise ratio show that the proposed method can effectively extract the crack and friction signals with input signal-to-noise ratio greater than -15dB. When the signal-to-noise ratio of mixed signal is -15dB, The output SNR of crack and friction signals were -1.31 and -1.36 dB, respectively, and the correlation coefficients were 0.62 and 0.63, respectively. The extraction effect was better than that of wavelet packet and FastICA separation method (SNR = -1.74 and -2.06 dB, the correlation coefficients were 0.59 and 0.59, respectively) and the single method was better than the method of combining wavelet packet and FastICA (SNR = -1.74 and -2.06 dB, respectively). The FastICA algorithm is used (signal-to-noise ratio (SNR) is -4.57 and -4.31 dB, the correlation coefficient is 0.17 and 0.19 respectively). Therefore, the proposed method of synthesizing WPA and ICA is a better multi-source weak signal extraction method.
【作者單位】： 長(zhǎng)沙理工大學(xué)工程車輛安全性設(shè)計(jì)與可靠性技術(shù)湖南省重點(diǎn)實(shí)驗(yàn)室;長(zhǎng)沙理工大學(xué)道路災(zāi)變防治及交通安全教育部工程研究中心;廣西大學(xué)機(jī)械工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(51105045,51205031,51365006) 湖南省教育廳重點(diǎn)項(xiàng)目(15A008) 長(zhǎng)沙理工大學(xué)工程車輛安全性設(shè)計(jì)與可靠性技術(shù)湖南省重點(diǎn)實(shí)驗(yàn)室基金(KF1507);長(zhǎng)沙理工大學(xué)道路災(zāi)變防治及交通安全教育部工程研究中心項(xiàng)目(kfj140407)資助
【分類號(hào)】：TH17
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本文編號(hào)：2081983