| 基于堆叠降噪自编码的刀具磨损状态识别 |
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| 引用本文： | 王丽华,杨家巍,张永宏,赵晓平,谢阳阳. 基于堆叠降噪自编码的刀具磨损状态识别[J]. 中国机械工程, 2018, 29(17): 2038 |
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| 作者姓名： | 王丽华 杨家巍 张永宏 赵晓平 谢阳阳 |
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| 作者单位： | 1.南京信息工程大学信息与控制学院,南京,2100442.南京信息工程大学计算机与软件学院,南京,210044 |
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| 基金项目： | 国家自然科学基金资助项目（51405241,51575283,51505234）National Natural Science Foundation of China （No. 51405241,51575283,51505234） |
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| 摘 要： | 提出了一种基于堆叠降噪自编码（SDAE）的刀具磨损状态识别方法。构建了SDAE神经网络来学习声发射（AE）信号的特征,并对自编码网络进行有监督的微调,从而对刀具磨损状态进行精确识别。实验结果表明,SDAE方法能够自适应地学习,得到有效的特征表达,且刀具磨损状态识别结果精确度高,该方法能够有效地进行刀具磨损状态识别。
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| 关 键 词： | 刀具磨损 声发射 深度学习 堆叠降噪自编码 |
Tool Wear Condition Recognition Based on SDAE |
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| WANG Lihua,YANG Jiawei,ZHANG Yonghong,ZHAO Xiaoping,XIE Yangyang. Tool Wear Condition Recognition Based on SDAE[J]. China Mechanical Engineering, 2018, 29(17): 2038 |
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| Authors: | WANG Lihua YANG Jiawei ZHANG Yonghong ZHAO Xiaoping XIE Yangyang |
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| Affiliation: | 1.School of Information and Control,Nanjing University of Information Science & Technology,Nanjing,2100442.School of Computer & Software,Nanjing University of Information Science & Technology,Nanjing,210044 |
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| Abstract: | A new method of condition recognition for tool wear was proposed based on SADE . A SDAE neural network was constructed to learn the characteristics of AE signals, and a supervised fine-tuning of the autoencoder network was carried out, so that the tool wear conditions were accurately recognized. The experimental results show that the SDAE method may learn adaptively to get effective feature expressions and the tool wear condition recognition precision is high. The proposed method may be used to recognize tool wear conditions effectively. |
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| Keywords: | tool wear acoustic emission（AE） deep learning stacked denoising autoencoder （SDAE） |
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