A Deep Nonnegative Matrix Factorization Approach via Autoencoder for Nonlinear Fault Detection
Ren, Zelin1,2; Zhang, Wensheng1,2; Zhang, Zhizhong1,2
发表期刊IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN1551-3203
2020-08
卷号16期号:8页码:5042-5052
摘要

In the era of big data, data-driven fault detection is vital for modern industrial systems. This article considers the potential complexity of fault detection and proposes a novel nonlinear method based on nonnegative matrix factorization (NMF). Motivated by an autoencoder, in this article we first utilize the input data to learn an appropriate nonlinear mapping function, which transforms the original space into a high-dimensional feature space. Then, according to the decomposition rule of NMF, we divide the learned feature space into two subspaces, and two statistics in these subspaces are designed appropriately for nonlinear fault detection. The established method, i.e., deep nonnegative matrix factorization (DNMF), is implemented by three parts: an encoder module, an NMF module, and a decoder module. Unlike conventional NMF-based nonlinear methods using implicit and predetermined kernels, DNMF provides a new nonlinear scheme applied to NMF via a deep autoencoder framework and realizes nonlinear mapping for input data automatically. Our proposed nonlinear framework can be further generalized to other linear methods. Besides, DNMF greatly expands the NMF application scope by breaking through the limitation of nonnegative input. The Tennessee Eastman process as an industrial benchmark is employed to verify the effectiveness of the proposed method.

关键词data-driven fault detection deep autoencoder nonlinear industrial process nonnegative matrix factorization (NMF)
DOI10.1109/TII.2019.2951011
关键词[WOS]DIAGNOSIS
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[61602484] ; National Natural Science Foundation of China[61876183] ; National Natural Science Foundation of China[U1636220] ; Beijing Municipal Natural Science Foundation[4172063] ; Beijing Municipal Natural Science Foundation[TII-19-1879]
项目资助者National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation
WOS研究方向Automation & Control Systems ; Computer Science ; Engineering
WOS类目Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS记录号WOS:000537198400007
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类人工智能+制造
引用统计
被引频次:30[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/39633
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Zhang, Wensheng
作者单位1.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.University of Chinese Academy of Sciences, Beijing 101408, China
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Ren, Zelin,Zhang, Wensheng,Zhang, Zhizhong. A Deep Nonnegative Matrix Factorization Approach via Autoencoder for Nonlinear Fault Detection[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2020,16(8):5042-5052.
APA Ren, Zelin,Zhang, Wensheng,&Zhang, Zhizhong.(2020).A Deep Nonnegative Matrix Factorization Approach via Autoencoder for Nonlinear Fault Detection.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,16(8),5042-5052.
MLA Ren, Zelin,et al."A Deep Nonnegative Matrix Factorization Approach via Autoencoder for Nonlinear Fault Detection".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 16.8(2020):5042-5052.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
RZL-A Deep Nonnegati(2122KB)期刊论文作者接受稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Ren, Zelin]的文章
[Zhang, Wensheng]的文章
[Zhang, Zhizhong]的文章
百度学术
百度学术中相似的文章
[Ren, Zelin]的文章
[Zhang, Wensheng]的文章
[Zhang, Zhizhong]的文章
必应学术
必应学术中相似的文章
[Ren, Zelin]的文章
[Zhang, Wensheng]的文章
[Zhang, Zhizhong]的文章
相关权益政策
暂无数据
收藏/分享
文件名: RZL-A Deep Nonnegative Matrix Factorization Approach via Autoencoder for Nonlinear Fault Detection.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。