本文選題：齒輪箱　切入點(diǎn)：局域均值分解　出處：《機(jī)械傳動(dòng)》2017年08期 　論文類(lèi)型：期刊論文

【摘要】：針對(duì)齒輪箱使用中常見(jiàn)的故障檢測(cè)與識(shí)別問(wèn)題,考慮到齒輪箱振動(dòng)響應(yīng)信號(hào)非線性、非平穩(wěn)的特性,提出基于局域均值分解(LMD)的近似熵和粒子群優(yōu)化的極限學(xué)習(xí)機(jī)(PSO-ELM)結(jié)合的齒輪箱故障診斷方法。首先,使用LMD分解方法對(duì)齒輪箱各工況的振動(dòng)信號(hào)進(jìn)行分解,結(jié)合相關(guān)系數(shù)選取反映主要故障信息的前4個(gè)PF分量。利用近似熵進(jìn)行定量描述,組成特征向量。最后用粒子群算法對(duì)ELM的輸入權(quán)值與隱含層神經(jīng)元閾值進(jìn)行優(yōu)化,建立PSO-ELM模型,并將近似熵特征值輸入到ELM和PSO-ELM模型中,對(duì)齒輪箱不同工況進(jìn)行故障識(shí)別與分類(lèi)。結(jié)果表明,基于LMD近似熵和粒子群優(yōu)化的ELM有更高的分類(lèi)正確率,驗(yàn)證了該方法的可行性。
[Abstract]:In view of the common problems of fault detection and identification in the use of gearbox, considering the nonlinear and non-stationary characteristics of the vibration response signal of the gearbox, An approximate entropy method based on local mean decomposition (LMD) and particle swarm optimization (PSO -ELM) based gearbox fault diagnosis method is proposed. Firstly, LMD decomposition method is used to decompose the vibration signals of the gearbox under different working conditions. The first four PF components reflecting the main fault information are selected according to the correlation coefficient. The approximate entropy is used to quantitatively describe and form the eigenvector. Finally, particle swarm optimization is used to optimize the input weights of ELM and the threshold of hidden layer neurons. The PSO-ELM model is established, and the approximate entropy eigenvalue is input into the ELM and PSO-ELM models to identify and classify the gearbox faults under different working conditions. The results show that the ELM based on LMD approximate entropy and particle swarm optimization has higher classification accuracy. The feasibility of the method is verified.
【作者單位】： 中北大學(xué)機(jī)械與動(dòng)力工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(51175480,50875247)
【分類(lèi)號(hào)】：TH132.41

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