Assessment of Fault Detection Performance and Computation Algorithms

2020 ◽  
pp. 479-490
Author(s):  
Steven X. Ding
Keyword(s):  
Fault Detection ◽  
Detection Performance ◽  
Computation Algorithms

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

2021 ◽  
Author(s):  
Wenping Zhang ◽  
Feng Liu ◽  
Zhenxing He ◽  
Lixin Xu ◽  
Guijun Hu
Keyword(s):  
Fault Detection ◽  
Detection Performance ◽  
Fiber Link ◽  
Multi Mode

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

Processes ◽  
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.

Model-Based and Data-Driven Fault Detection Performance for a Small UAV

2013 ◽  
Vol 18 (4) ◽  
pp. 1300-1309 ◽  
Author(s):  
Paul Freeman ◽  
Rohit Pandita ◽  
Nisheeth Srivastava ◽  
Gary J. Balas
Keyword(s):  
Fault Detection ◽  
Detection Performance ◽  
Data Driven ◽  
Model Based ◽  
Small Uav

An Online Outlier Identification and Removal Scheme for Improving Fault Detection Performance

2014 ◽  
Vol 25 (5) ◽  
pp. 908-919 ◽  
Author(s):  
Hasan Ferdowsi ◽  
Sarangapani Jagannathan ◽  
Maciej Zawodniok
Keyword(s):  
Fault Detection ◽  
Detection Performance ◽  
Outlier Identification

scholarly journals Fault Detection Performance Analysis of GNSS Integrity RAIM

2012 ◽  
Vol 20 (3) ◽  
pp. 49-56
Author(s):  
Ji Hye Kim ◽  
Kwan Dong Park ◽  
Du Sik Kim
Keyword(s):  
Performance Analysis ◽  
Fault Detection ◽  
Detection Performance
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