Knowledge Commons of Institute of Automation,CAS
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 |
ISSN | 1551-3203 |
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) |
DOI | 10.1109/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 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39633 |
专题 | 多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队 |
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. |
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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 |
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