本文選題：刮板輸送機　切入點：卷積神經(jīng)網(wǎng)絡(luò)　出處：《煤炭科學(xué)技術(shù)》2017年05期 　論文類型：期刊論文

【摘要】：針對刮板輸送機在其彎曲區(qū)段容易發(fā)生的飄鏈問題,提出了一種基于卷積神經(jīng)網(wǎng)絡(luò)和支持向量機的聲音信號識別模型,該模型以經(jīng)過PCA白化處理的綜采工作面設(shè)備聲音運行聲音的聲譜圖為輸入,由深度CNN網(wǎng)絡(luò)提取聲音信號的特征,并以SVM分類器實現(xiàn)對聲音信號的識別,最終實現(xiàn)對刮板輸送機飄鏈故障的診斷。同時推導(dǎo)了以SVM為輸出層的深度CNN網(wǎng)絡(luò)模型在訓(xùn)練時誤差反向傳播時輸出層對全連接層的敏感度函數(shù),并通過試驗發(fā)現(xiàn)了對輸入的聲音信號進行不同時長的切分作為模型輸入時,對CNN-SVM模型識別率產(chǎn)生影響的規(guī)律,最后通過對比試驗驗證了此模型確實比傳統(tǒng)的GMM-HMM模型具有更高的識別準確率。
[Abstract]:Aiming at the floating chain problem of scraper conveyor in its bending section, a sound signal recognition model based on convolution neural network and support vector machine is proposed. In this model, the sound spectrum of the sound running sound of the equipment in the fully mechanized mining face after PCA whitening is taken as input, and the feature of the sound signal is extracted from the depth CNN network, and the recognition of the sound signal is realized by using the SVM classifier. Finally, the fault diagnosis of floating chain of scraper conveyor is realized. At the same time, the sensitivity function of the output layer to the full connection layer is derived when the error is back propagated by the depth CNN network model with SVM as the output layer. And through the experiment, we find the rule that the CNN-SVM model recognition rate is influenced by the different time segmentation of the input sound signal as the model input. Finally, the comparison experiment shows that the model has higher recognition accuracy than the traditional GMM-HMM model.
【作者單位】： 西安科技大學(xué)機械工程學(xué)院;平頂山天安煤業(yè)股份有限公司六礦;
【基金】：國家自然科學(xué)基金資助項目(U1361121)
【分類號】：TD528.3;TN912.34

【參考文獻】

相關(guān)期刊論文 前7條

1 Ralston Jonathon C.;Reid David C.;Dunn Mark T.;Hainsworth David W.;;Longwall automation: Delivering enabling technology to achieve safer and more productive underground mining[J];International Journal of Mining Science and Technology;2015年06期

2 張智U，

本文編號：1650772