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

【摘要】：針對刀具磨損過程中產(chǎn)生聲發(fā)射信號的不確定性以及神經(jīng)網(wǎng)絡學習算法收斂速度慢、易陷入局部極小值、對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的刀具磨損狀態(tài)識別方法。首先,對聲發(fā)射信號進行小波包分解與重構,濾除干擾頻段對求取特征參數(shù)的影響;其次,對重構后的信號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)射信號的云特性及磨損狀態(tài)與云特征參數(shù)之間的關系;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進行識別。研究結果表明:所提取的特征可以很好地反映刀具的磨損狀態(tài),云-支持向量機方法可以有效地實現(xiàn)刀具磨損狀態(tài)的識別,與傳統(tǒng)神經(jīng)網(wǎng)絡識別方法相比具有更高的識別率,識別率達到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】： 東北電力大學機械工程學院;
【基金】：吉林省科技廳科技公關計劃資助項目(20140204004SF) 吉林省教育廳“十二五”科學技術研究資助項目(20150249)
【分類號】：TG71;TP18
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本文編號：1563045