Use of neural networks for sensor failure detection in a control system

1990 ◽  
Vol 10 (3) ◽  
pp. 49-55 ◽  
Author(s):  
S.R. Naidu ◽  
E. Zafiriou ◽  
T.J. McAvoy
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Failure Detection ◽  
Sensor Failure

scholarly journals Design, Sensing and Control of a Robotic Prosthetic Eye for Natural Eye Movement

2006 ◽  
Vol 3 (1) ◽  
pp. 29-41 ◽  
Author(s):  
J. J. Gu ◽  
M. Meng ◽  
A. Cook ◽  
P. X. Liu
Keyword(s):  
Control System ◽  
Eye Movement ◽  
Experimental Studies ◽  
Failure Detection ◽  
Sensor Data ◽  
Generation Model ◽  
Movement Detection ◽  
Sensor Failure ◽  
And Control ◽  
Prosthetic Eye

Loss of an eye is a tragedy for a person, who may suffer psychologically and physically. This paper is concerned with the design, sensing and control of a robotic prosthetic eye that moves horizontally in synchronization with the movement of the natural eye. Two generations of robotic prosthetic eye models have been developed. The first generation model uses an external infrared sensor array mounted on the frame of a pair of eyeglasses to detect the natural eye movement and to feed the control system to drive the artificial eye to move with the natural eye. The second generation model removes the impractical usage of the eye glass frame and uses the human brain EOG (electro-ocular-graph) signal picked up by electrodes placed on the sides of a person's temple to carry out the same eye movement detection and control tasks as mentioned above. Theoretical issues on sensor failure detection and recovery, and signal processing techniques used in sensor data fusion, are studied using statistical methods and artificial neural network based techniques. In addition, practical control system design and implementation using micro-controllers are studied and implemented to carry out the natural eye movement detection and artificial robotic eye control tasks. Simulation and experimental studies are performed, and the results are included to demonstrate the effectiveness of the research project reported in this paper.

Agent-based approach to continuous process control for enabling parallelization of engineering cycles

2017 ◽  
Vol 26 (3) ◽  
pp. 287-298
Author(s):  
Grzegorz Polaków ◽  
Jacek Czeczot ◽  
Piotr Laszczyk
Keyword(s):  
Control System ◽  
Production Systems ◽  
Expert Knowledge ◽  
Continuous Process ◽  
Design Procedure ◽  
Cost Savings ◽  
Failure Detection ◽  
Sensor Failure ◽  
Agent Based ◽  
Control Loops

Modern manufacturing and production systems have growing demands in energy and cost savings, which can be ensured using more advanced control algorithms at the regulatory-level industrial control loops. However, developing such algorithms requires case-dependent approach that involves complex mathematics and expertise in various fields of technology and engineering. Gathering all the needed experts to conduct the control system engineering cycle is nearly impossible for organizational and economic reasons. Thus, in this work, it is proposed to employ an agent-based approach, which is substantially different than the conventional engineering cycles for developing the static control system. The idea is to split the entire control design procedure into smaller tasks of developing the modules (i.e. agents), which encapsulate the expert knowledge (e.g. on sensor failure detection, input signal modelling and estimation). It enables clear division of the competences between the experts and allows for dynamic inclusion of the expert knowledge into the newly designed or already existing system. Therefore, the expertise may be distributed both in location and in time, which stands in contradiction to the static approach based on sequential engineering cycles.

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

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