【摘要】：為了消除噪聲對(duì)齒輪傳動(dòng)系統(tǒng)故障特征提取的影響,提出了一種基于集成經(jīng)驗(yàn)?zāi)B(tài)分解(ensemble empirical mode decomposition,簡(jiǎn)稱EEMD)和時(shí)頻峰值濾波(time-frequency peak filtering,簡(jiǎn)稱TFPF)相結(jié)合的降噪方法。針對(duì)TFPF算法在窗長(zhǎng)的選擇方面受到限制的問(wèn)題,采用了EEMD方法對(duì)其進(jìn)行改進(jìn),使得信號(hào)在噪聲壓制和有效信號(hào)保真兩方面得到權(quán)衡;含噪聲的信號(hào)經(jīng)過(guò)EEMD分解后,得到一系列頻率成分從高到低的本征模態(tài)函數(shù)(intrinsic mode functions,簡(jiǎn)稱IMFs),計(jì)算出各IMFs間的相關(guān)系數(shù),判斷需要濾波的IMFs。對(duì)不同的IMFs選擇不同的窗長(zhǎng)進(jìn)行TFPF濾波,把過(guò)濾后的IMFs和剩余的IMFs重構(gòu)得到最終的降噪信號(hào)。用模擬仿真信號(hào)和齒輪齒根故障信號(hào)對(duì)該方法進(jìn)行驗(yàn)證,可見EEMD+TFPF能有效地去除噪聲,成功提取齒根裂紋故障特征。
[Abstract]:In order to eliminate the influence of noise on fault feature extraction of gear transmission system, a noise reduction method based on integrated empirical mode decomposition (ensemble empirical mode decomposition, (EEMD) and time-frequency peak filtering (TFPF) is proposed. In order to solve the problem that the selection of window length of TFPF algorithm is limited, the EEMD method is used to improve it, which makes the signal balance between noise suppression and effective signal fidelity. After the signal with noise is decomposed by EEMD, a series of intrinsic modal functions (intrinsic mode functions, with frequency components from high to low are obtained to calculate the correlation coefficients among IMFs, and the IMFs. that needs to be filtered is judged. Different IMFs window lengths are selected for TFPF filtering, and the filtered IMFs and the remaining IMFs are reconstructed to obtain the final noise reduction signal. The simulation signal and gear root fault signal are used to verify the method. It can be seen that EEMD TFPF can effectively remove noise and successfully extract the fault characteristics of tooth root crack.
【作者單位】： 太原理工大學(xué)機(jī)械工程學(xué)院;太原科技大學(xué)機(jī)械工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(50775157) 山西省基礎(chǔ)研究資助項(xiàng)目(2012011012-1)
【分類號(hào)】：TH132.41;TH17

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1 王少君;基于EEMD的滾動(dòng)軸承微弱故障特征提取方法研究[D];石家莊鐵道大學(xué);2016年

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