Fault detection performance of multi-mode transmission reflection analysis for few-mode fiber link

A novel method of the fiber-link fault detection and location with multi-channel and multi-fiber based on chaotic light theory

2014 7th International Conference on Biomedical Engineering and Informatics ◽

10.1109/bmei.2014.7002911 ◽

2014 ◽

Author(s):

Xinyu Dou ◽

Hehe Yue ◽

Yusong Zhou ◽

Hongxi Yin ◽

Chenguang Wu ◽

...

Keyword(s):

Fault Detection ◽

Novel Method ◽

Fiber Link ◽

Light Theory

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IMPROVING EARTH-FAULT DETECTION PERFORMANCE AND SUPPLY SECURITY OF CABLED RURAL MV-NETWORKS WITH FAULT ISOLATION

10.1049/icp.2021.2160 ◽

2021 ◽

Author(s):

A. Wahlroos ◽

J. Altonen ◽

H.-M. Aalto

Keyword(s):

Fault Detection ◽

Detection Performance ◽

Fault Isolation ◽

Earth Fault

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Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process

Processes ◽

10.3390/pr10010122 ◽

2022 ◽

Vol 10 (1) ◽

pp. 122

Author(s):

Yang Li ◽

Fangyuan Ma ◽

Cheng Ji ◽

Jingde Wang ◽

Wei Sun

Keyword(s):

Feature Extraction ◽

Fault Detection ◽

Chemical Process ◽

Detection Method ◽

Detection Performance ◽

Detection Methods ◽

Normal Operation ◽

Discriminative Feature ◽

Inherent Feature ◽

Global And Local

Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then, multiple marginal fisher analysis (MMFA) is introduced to extract the discriminative feature, which can better separate normal data from fault data. On the basis of fisher framework, GLPP and MMFA are integrated to extract inherent and discriminative features of the data simultaneously. Furthermore, fault detection methods based on GLMDPP are constructed and applied to the Tennessee Eastman (TE) process. Compared with the PCA and GLPP method, the effectiveness of the proposed method in fault detection is validated with the result of TE process.

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