Autoencoder-based fault diagnosis for grinding system

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 工业控制网络与系统研究室

	Autoencoder-based fault diagnosis for grinding system
	Fu, Dongdong ; Xu, Chengcheng ; Qu XY(曲星宇); Zeng P(曾鹏)
	2017
会议名称	29th Chinese Control and Decision Conference, CCDC 2017
会议日期	May 28-30, 2017
会议地点	Chongqing, China
关键词	Fault Diagnosis Autoencoder Softmax Classifier Deep Learning
页码	3867-3872
通讯作者	Qu XY(曲星宇)
中文摘要	At present, most fault diagnosis for grinding system is based on artificial judgments, which is inefficient, low accurate, high cost and easy to cause casualties. The traditional neural network has an unsatisfying performance to predict on high dimensional dataset, and is hard to extract crucial features, which brings about terrible classification results. To solve the above problems, the paper present a deep learning based on autoencoder to realize the intelligent diagnosis for grinding system. The algorithm applies autoencoder to extract features from fault dataset, and transit the non-linearized features to Softmax classification to recognize the fault category. This paper compares autoencoder-based deep learning networks and the traditional BP neural networks in experiments, and it is concluded that the autoencoder-based deep learning outperforms BP networks in the unbalanced classification. The classification precision is up to 92.4% by using the proposed method.
收录类别	EI ; CPCI（ISTP）
产权排序	1
会议录	Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISSN号	1948-9439
ISBN号	978-1-5090-4656-0
WOS记录号	WOS:000427082206080
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/20857]
专题	沈阳自动化研究所_工业控制网络与系统研究室
作者单位	1.University of Chinese Academy of Sciences, Beijing, 100049, China 2.Key Lab of Networked Control System, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, 110016, China 3.Northern Heavy Industries Group Co. Itd, Shenyang, 110860, China 4.College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China
推荐引用方式 GB/T 7714	Fu, Dongdong,Xu, Chengcheng,Qu XY,et al. Autoencoder-based fault diagnosis for grinding system[C]. 见:29th Chinese Control and Decision Conference, CCDC 2017. Chongqing, China. May 28-30, 2017.