基于新型无监督深度神经网络(DNN)的刀具状态监测研究

IF 0.7 Q4 ENGINEERING, MECHANICAL
Jingjing Gao, Jing Liu, Xinli Yu
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引用次数: 0

摘要

为了提高刀具磨损监测的识别精度和准确度,提出了一种基于堆栈去噪自编码器(SDA)的无监督深度神经网络(DNN)。经过特征提取和选择,堆栈去噪自动编码网络降低特征向量的维数。在此基础上,利用主成分分析(PCA)和t分布随机邻居嵌入(t-SNE)对特征进行两次降维,最终得到一个简单的二维特征矩阵。最后,通过增加SoftMax回归层建立SDA深度神经网络模型,并将刀具磨损监测结果作为新的标记数据,通过二次反向传播对深度神经网络参数进行微调。实验结果表明,该方法能够自适应学习并获得有效的特征表达,刀具磨损状态识别结果具有较高的准确性。该方法能有效识别刀具的磨损状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on tool condition monitoring (TCM) using a novel unsupervised deep neural network (DNN)
In order to improve the recognition precision and accuracy of tool wear monitoring, an unsupervised deep neural network (DNN) based on stack denoising autoencoder (SDA) is proposed. After feature extraction and selection, the stack denoising automatic coding network reduces the dimensionality of the feature vector. On this basis, principal component analysis (PCA) and T-distributed random neighbor embedding (t-SNE) are used to reduce the dimensionality of the features twice, and finally a simple two-dimensional feature matrix is obtained. Finally, the deep neural network model of SDA is established by adding SoftMax regression layer, and the tool wear monitoring results are taken as new labeled data, and the deep neural network parameters are fine-tuned by secondary backpropagation. The experimental results show that the proposed method can learn adaptively and obtain effective feature expression, and the tool wear state recognition results are highly accurate. The proposed method can effectively identify the tool wear state.
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来源期刊
Journal of Vibroengineering
Journal of Vibroengineering 工程技术-工程:机械
CiteScore
1.70
自引率
0.00%
发文量
97
审稿时长
4.5 months
期刊介绍: Journal of VIBROENGINEERING (JVE) ISSN 1392-8716 is a prestigious peer reviewed International Journal specializing in theoretical and practical aspects of Vibration Engineering. It is indexed in ESCI and other major databases. Published every 1.5 months (8 times yearly), the journal attracts attention from the International Engineering Community.
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