本文關(guān)鍵詞： 采煤機(jī)齒輪箱 故障特征 分解效率 改進(jìn)小波去噪 集合經(jīng)驗(yàn)?zāi)B(tài)分解 行星輪 模態(tài)混疊　出處：《中南大學(xué)學(xué)報(bào)(自然科學(xué)版)》2016年10期 　論文類(lèi)型：期刊論文

【摘要】：針對(duì)采煤現(xiàn)場(chǎng)強(qiáng)噪聲背景下采煤機(jī)齒輪箱振動(dòng)信號(hào)集合經(jīng)驗(yàn)?zāi)B(tài)分解(EEMD)故障特征不明顯和分解效率較低的問(wèn)題,提出基于改進(jìn)小波去噪預(yù)處理和EEMD的故障診斷方法。采用小波改進(jìn)閾值函數(shù)法對(duì)振動(dòng)信號(hào)進(jìn)行去噪預(yù)處理,與傳統(tǒng)小波閾值函數(shù)法相比能夠有效地提高信號(hào)的信噪比。對(duì)去噪后的信號(hào)進(jìn)行EEMD分解得到若干個(gè)本征模態(tài)分量(IMF),計(jì)算各IMF分量的相關(guān)度并剔除虛假分量。將該方法應(yīng)用于采煤機(jī)齒輪箱行星輪的故障診斷,通過(guò)對(duì)真實(shí)的IMF分量進(jìn)行頻譜分析并提取信號(hào)的故障特征頻率,與未去噪的信號(hào)進(jìn)行對(duì)比。研究結(jié)果表明:該方法能夠突出故障特征頻率,使分解效率提高17.35%,并能進(jìn)一步減小模態(tài)混疊現(xiàn)象。
[Abstract]:In view of the problem that the fault characteristics of EEMDD of shearer gear box vibration signal set are not obvious and the decomposition efficiency is low under the background of strong noise in coal mining field, A fault diagnosis method based on improved wavelet denoising preprocessing and EEMD is proposed. Compared with the traditional wavelet threshold function method, the signal-to-noise ratio (SNR) of the signal can be improved effectively. Several intrinsic mode components are obtained by EEMD decomposition of the de-noised signal, the correlation of each IMF component is calculated and the false component is eliminated. The method is applied to fault diagnosis of planetary gear in shearer gear box. By analyzing the true IMF component and extracting the fault characteristic frequency of the signal, the research results show that the method can outshine the fault feature frequency. The decomposition efficiency is increased by 17.35 and the mode aliasing can be further reduced.
【作者單位】： 中南大學(xué)機(jī)電工程學(xué)院;高性能復(fù)雜制造國(guó)家重點(diǎn)實(shí)驗(yàn)室;深海礦產(chǎn)資源開(kāi)發(fā)利用技術(shù)國(guó)家重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家重點(diǎn)基礎(chǔ)研究發(fā)展計(jì)劃(973計(jì)劃)項(xiàng)目(2014CB046305) 國(guó)家大洋專(zhuān)項(xiàng)項(xiàng)目(DY125-14-T-03)~~
【分類(lèi)號(hào)】：TD421.6

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