Intelligent Condition Monitoring Systems for an AUV Robot

Intelligent Condition Monitoring Systems for an AUV Robot    

Journal : Applied Mechanics and Materials (Volumes 152 - 154)
Volume     : Mechanical Engineering and Materials
Authors : Amir Parsa Anvar, Manjit S. Garcha, Ritchie D. Saliba, Taranjit M. Singh, Amir M. Anvar, Steven Grainger

January, 2012

Abstract:

This paper discuses intelligent techniques used to monitor and correct operational abnormalities in Autonomous Underwater Vehicles. Neural Networks are usually utilised in the diagnosis section, while Fuzzy Logic is implemented in the prognosis and remedy sections. The performance of an AUV’s sub-system has a great affect on the overall success of the vehicle. Once a sub-system becomes faulty, the various components associated with the control of the AUV may get influenced, which can degrade the overall performance of the integrated system or make it invalid altogether . Such failures may result in large amounts of wasted time, loss of data and increases in mission costs.

FannTool ;

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These input values were fed into the Fast Artificial Neural Network
(FANN) toolbox, FannTool-1.1, to provide simulations of a Neural Network for the inputand output across each sensor.
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Control of a Thermoelectric Brain Cooler by Adaptive Neuro-Fuzzy Inference System

Instrumentation Science & Technology
Volume 36, Issue 6, 2008

Control of a Thermoelectric Brain Cooler by Adaptive Neuro-Fuzzy Inference System
R. Ahiska , A. H. Yavuz , M. Kaymaz  & İ. Güler

Abstract:

In this study, neuro-fuzzy control of a thermoelectric head cooler
system (thermoelectric helmet) is developed for brain hypothermia applications.
Hypothermia is a medical treatment method of protecting the brain, in which
the temperature of the brain drops below the critical level for reducing oxygen
consumption of tissues. The brain should be kept at a certain temperature by a
suitable control for hypothermia applications. The temperature of the thermoelectric
head cooler system changes according to the current intensity supplied.
The control of the thermoelectric head cooler system was performed according
to the initial membership functions, which was determined by an expert using
fuzzy logic control. The system was modeled by an adaptive neuro-fuzzy inference
system (ANFIS). The data were then entered into the system and new membership
functions were determined. By this way, learning ability of the artificial
neural network and the abilities of fuzzy logic, such as decision making, were
combined and a more effective solution was developed. The system software
can be reprogrammed with the new membership functions.

FannTool :

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The data files obtained are shown in Table 4. An ANN model
(4 >2 >1) was projected with Fast Artificial Neural Network Tool
(FannTool version 0.60) by using this data file and has been trained. An
ANN trained was recorded and an interface unit was projected by using
C programming language (Figure 6).
...

 

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