發(fā)布時(shí)間：2018-10-31 20:55

【摘要】：為實(shí)現(xiàn)對蝸輪蝸桿減速器工作過程中蝸輪磨損程度的精確監(jiān)測,利用多通道聲發(fā)射檢測儀器對不同磨損程度的蝸輪聲發(fā)射信號進(jìn)行在線采集。采用小波分析方法對信號進(jìn)行去噪處理,提取聲發(fā)射特征信號值,根據(jù)最小模糊熵優(yōu)化模型構(gòu)造出不同磨損程度蝸輪的模糊隸屬度函數(shù)。采用ANFIS多維模糊神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn)多通道聲發(fā)射信號的決策融合,提高了蝸輪磨損程度識別結(jié)果的準(zhǔn)確性。通過對隨機(jī)磨損程度的蝸輪進(jìn)行實(shí)際驗(yàn)證,實(shí)驗(yàn)結(jié)果驗(yàn)證了系統(tǒng)的有效性和可靠性。
[Abstract]:In order to accurately monitor the wear degree of worm gear in the working process of worm gear reducer, the acoustic emission signals of worm gear with different wear degree are collected on line by means of multi-channel acoustic emission detection instrument. The wavelet analysis method is used to de-noising the signal, and the acoustic emission characteristic signal is extracted. According to the minimum fuzzy entropy optimization model, the fuzzy membership function of the wormgear with different wear degree is constructed. The multi-channel acoustic emission signal decision fusion is realized by using ANFIS multi-dimensional fuzzy neural network, and the accuracy of the recognition result of worm-gear wear degree is improved. The validity and reliability of the system are verified by the actual verification of the worm gear with random wear degree.
【作者單位】： 遼寧工程技術(shù)大學(xué)機(jī)械工程學(xué)院;大連理工大學(xué)工業(yè)裝備結(jié)構(gòu)分析國家重點(diǎn)實(shí)驗(yàn)室;重慶大學(xué)機(jī)械傳動國家重點(diǎn)實(shí)驗(yàn)室;中國煤礦機(jī)械裝備有限責(zé)任公司;
【基金】：國家自然科學(xué)基金(51504121) 遼寧省自然科學(xué)基金(201601324)資助項(xiàng)目 遼寧“百千萬人才工程”培養(yǎng)經(jīng)費(fèi)(2014921070) 遼寧省優(yōu)秀人才支持計(jì)劃(LJQ2014036)資助項(xiàng)目
【分類號】：TH132.44

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