本文選題：刀具磨損　切入點(diǎn)：狀態(tài)識(shí)別　出處：《振動(dòng).測試與診斷》2017年05期 　論文類型：期刊論文

【摘要】：針對(duì)刀具磨損過程中產(chǎn)生聲發(fā)射信號(hào)的不確定性以及神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)算法收斂速度慢、易陷入局部極小值、對(duì)特征要求較高等問題,提出了基于云理論和最小二乘支持向量機(jī)的刀具磨損狀態(tài)識(shí)別方法。首先,對(duì)聲發(fā)射信號(hào)進(jìn)行小波包分解與重構(gòu),濾除干擾頻段對(duì)求取特征參數(shù)的影響;其次,對(duì)重構(gòu)后的信號(hào)利用逆向云算法提取云特征參數(shù):期望、熵、超熵,分析刀具磨損聲發(fā)射信號(hào)的云特性及磨損狀態(tài)與云特征參數(shù)之間的關(guān)系;最后,將云特征參數(shù)組成特征向量送入最小二乘支持向量機(jī)進(jìn)行識(shí)別。研究結(jié)果表明:所提取的特征可以很好地反映刀具的磨損狀態(tài),云-支持向量機(jī)方法可以有效地實(shí)現(xiàn)刀具磨損狀態(tài)的識(shí)別,與傳統(tǒng)神經(jīng)網(wǎng)絡(luò)識(shí)別方法相比具有更高的識(shí)別率,識(shí)別率達(dá)到96.67%。
[Abstract]:Aiming at the uncertainty of acoustic emission signal produced in tool wear process and the slow convergence speed of neural network learning algorithm, it is easy to fall into local minimum value, and the characteristic requirement is high. A tool wear recognition method based on cloud theory and least squares support vector machine (LS-SVM) is proposed. Firstly, the acoustic emission signal is decomposed and reconstructed by wavelet packet, and the influence of interference band on obtaining characteristic parameters is filtered. Using reverse cloud algorithm to extract cloud characteristic parameters: expectation, entropy, excess entropy, analyze the cloud characteristics of tool wear acoustic emission signal and the relationship between wear state and cloud characteristic parameters. The cloud feature parameters are input into the least squares support vector machine for recognition. The results show that the extracted features can well reflect the tool wear state. The cloud-support vector machine method can effectively realize tool wear recognition. Compared with the traditional neural network recognition method, it has a higher recognition rate and the recognition rate reaches 96.67.
【作者單位】： 東北電力大學(xué)機(jī)械工程學(xué)院;
【基金】：吉林省科技廳科技公關(guān)計(jì)劃資助項(xiàng)目(20140204004SF) 吉林省教育廳“十二五”科學(xué)技術(shù)研究資助項(xiàng)目(20150249)
【分類號(hào)】：TG71;TP18
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本文編號(hào)：1563045