Fault Identification for I.C. Engines Using Artificial Neural Network

2011 ◽  
Author(s):  
Manthan Shah ◽  
Vijay Gaikwad ◽  
Shashikant Lokhande ◽  
Sanket Borhade
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Identification ◽  
Artificial Neural

scholarly journals Fault Identification by Extreme Pressure in Oil Pipelines Using Artificial Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 90-96
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Contextual Information ◽  
Training Data ◽  
Fault Identification ◽  
Pipeline System ◽  
Extreme Pressure ◽  
Feed Forward ◽  
Oil Pipeline ◽  
Artificial Neural

Conventional Artificial Neural Network approaches such as Feed-Forward Networks has been used in diverse applications but are not naturally predictive and also require supervised learning. Feed-forward Artificial Neural Network also trained by backpropagation poses the problem of varnishing gradient. Algorithm using Gaussian membership function with a context-decision gate for detection operations has been proposed as an alternative to the traditional Feed Forward Architecture. The AI monitoring System shows promising results in solving many recurrent problems, particularly those requiring long-term storage dependencies - the Vanishing Gradient problem (VGP) and has the ability to use contextual information when mapping between input and output sequences. The Oil monitoring system employs dynamic data flow modeling to simulate the behavior of probably militant behaviors. The contextual information (context data) includes such context as Pressure from the lab scale experimental setup of oil pipeline system. In this approach, not only the networks are trained to adapt to the given training data, the training data (the expected outputs of fault indices) is also updated to adapt to the neural network. During the training procedure, both the neural networks and training data are updated interactively. Dynamic simulations were performed using a real-time data obtained from the Radial Bias Kernel Network. The data is tested using AI system in MATLAB-SIMULINK environment to verify the performance of the proposed system. The results were promising indicating the real state of fault identification in oil pipeline system caused by extreme pressure during transportation.

Monitoring and Fault Identification in Aeronautical Structures Using an ARTMAP-Fuzzy-Wavelet Artificial Neural Network

2014 ◽  
Vol 1025-1026 ◽  
pp. 1107-1112 ◽  
Author(s):  
Fernando Parra dos Anjos Lima ◽  
Simone Silva Frutuoso de Souza ◽  
Fábio Roberto Chavarette ◽  
Mara Lúcia Martins Lopes ◽  
Antonio Eduardo Turra ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Numerical Model ◽  
Fault Identification ◽  
Structural Failure ◽  
Aircraft Structures ◽  
Artificial Neural ◽  
Aluminum Beam

This paper presents a methodology to perform the monitoring and identification of flaws in aircraft structures using an ARTMAP-Fuzzy-Wavelet artificial neural network. This technique is used in the detection and characterization of structural failure. The main application of this method is to assist in the inspection of aircraft structures in order to identify and characterize failures as well as decision-making, in order to avoid accidents or air crashes. In order to evaluate this method, the modeling and simulation of signals from a numerical model of an aluminum beam was performed. The results obtained by the method are satisfactory compared to literature.

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