Modeling of Corrosion Degradation for PCCP for Condition Monitoring Simulation Using Equivalent Circuit and Artificial Neural Networks (A Simulation Study in GMRA)

2012 ◽  
pp. 436-443
Author(s):  
Youssef Hamad Shakmak ◽  
Saleh Hussein Awami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Equivalent Circuit ◽  
Condition Monitoring ◽  
Simulation Study ◽  
Artificial Neural

scholarly journals Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks

Entropy ◽  
2018 ◽  
Vol 20 (4) ◽  
pp. 249 ◽  
Author(s):  
Krzysztof Gajowniczek ◽  
Arkadiusz Orłowski ◽  
Tomasz Ząbkowski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulation Study ◽  
Generalized Entropy ◽  
Artificial Neural

Condition Monitoring of an Electrohydraulic Position Control System Using Artificial Neural Networks

2004 ◽  
Author(s):  
K. Pollmeier ◽  
C. R. Burrows ◽  
K. A. Edge
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Condition Monitoring ◽  
Position Control ◽  
Linear Actuator ◽  
Modular Approach ◽  
Failure Characteristics ◽  
Actuator System ◽  
Artificial Neural ◽  
Position Control System

This paper investigates the condition monitoring of a servo-valve-controlled linear actuator system using artificial neural networks (NNs). The aim is to discuss techniques for the identification of failure characteristics and their source. It is shown that neural networks can be trained to identify more than one fault but these are larger and require more training patterns than networks for single fault diagnosis. This leads to much longer training times and to problems with scaleability. Therefore a modular approach has been developed. Several networks were trained each to identify an individual fault. The parallel outputs of these nets were then used as inputs to another network. This additional network was able to identify not only the correct faults but also the actual fault levels.

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