Fault Detection Technique for Electromechanical Actuator of the Aircraft Using Neural Networks

2021 ◽  
pp. 519-528
Author(s):  
Georgy Veresnikov ◽  
Valentin Lebedev ◽  
Oleg Ogorodnikov ◽  
Artem Golev
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Detection Technique ◽  
Electromechanical Actuator

scholarly journals Fault detection using neural network

2017 ◽  
Vol 1 (1) ◽  
pp. 70
Author(s):  
Elistia Liza Namigo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Seismic Data ◽  
The Other ◽  
Detection Technique ◽  
Occurrence Probability ◽  
Data Volume ◽  
Data Result

<p class="Abstract">Fault detection technique using neural networks have been successfully applied to a seismic data volume. This technique  is basically creating  a volume that highlights faults by combining the information from several fault indicators attributes (i.e. similarity, curvature and energy) into fault occurrence probability. This is performed by training a neural network on  two sets of attributes extracted at sample  locations picked manually -  one set  represents the fault class and the other represents the non-fault class. The next step is to apply the trained artificial neural network on the seismic data. Result indicates that faults are more highlighted and have better continuity since the surrounding noise  are mostly suppressed.</p>

Fault detection using neural network

2017 ◽  
Vol 1 (1) ◽  
pp. 70
Author(s):  
Elistia Liza Namigo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Seismic Data ◽  
The Other ◽  
Detection Technique ◽  
Occurrence Probability ◽  
Data Volume ◽  
Data Result

<p class="Abstract">Fault detection technique using neural networks have been successfully applied to a seismic data volume. This technique  is basically creating  a volume that highlights faults by combining the information from several fault indicators attributes (i.e. similarity, curvature and energy) into fault occurrence probability. This is performed by training a neural network on  two sets of attributes extracted at sample  locations picked manually -  one set  represents the fault class and the other represents the non-fault class. The next step is to apply the trained artificial neural network on the seismic data. Result indicates that faults are more highlighted and have better continuity since the surrounding noise  are mostly suppressed. </p>

An online winding fault detection technique for fault-tolerant PM machines

2009 ◽  
Vol 40 (3) ◽  
pp. 289-296 ◽  
Author(s):  
Z. Sun ◽  
J. Wang ◽  
D. Howe ◽  
G.W. Jewell
Keyword(s):  
Fault Detection ◽  
Fault Tolerant ◽  
Detection Technique
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