A Multihead Attention Self-Supervised Representation Model for Industrial Sensors Anomaly Detection-Reference-Cited by-同舟云学术

A Multihead Attention Self-Supervised Representation Model for Industrial Sensors Anomaly Detection

Published:2024-02 Issue:2 Volume:20 Page:2190-2199
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Qiao Yiqun¹^ORCID,Lü Jinhu²^ORCID,Wang Tian³^ORCID,Liu Kexin²^ORCID,Zhang Baochang³^ORCID,Snoussi Hichem⁴^ORCID

Affiliation:

1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

2. School of Automation Science and Electrical Engineering, State Key Laboratory of Software Development Environment, Beihang University, Beijing, China

3. Institute of Artificial Intelligence, the State Key Laboratory of Software Development Environment, Beihang University, Beijing, China

4. Charles Delaunay Institute (ICD), University of Technology of Troyes, Troyes, France

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Beijing Nova Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/10411967/10159231.pdf?arnumber=10159231

Reference43 articles.

1. A one-class generative adversarial detection framework for multifunctional fault diagnoses

2. LSTM Learning With Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT

3. A time-varying complex dynamical network model and its controlled synchronization criteria

4. Kalman Filtering and Neural Networks

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