By Topic

A Neural Network Multiagent Architecture Applied to Industrial Networks for Dynamic Allocation of Control Strategies Using Standard Function Blocks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Machado, V. ; Dept. of Inf. & Stat., Fed. Univ. of Piaui, Teresina, Brazil ; Doria Neto, A.D. ; de Melo, J.D.

This paper presents a multiagent architecture applied to factory automation. These agents detect faults in automated processes and allocate intelligent algorithms in field device function blocks (FBs) to solve these faults. We also present a dynamic FB parameter exchange strategy that allows agent fieldbus allocation. This architecture is a foundation for intelligent physical agents standard-based agent platform developed using Foundation Fieldbus technology. The aim is to enable problem detection activities, independent of user intervention. The use of artificial neural network (ANN)-based algorithms enables the agents to learn about fault patterns and adapt an algorithm that can be used in fault situations. Thus, we intend to reduce supervisor intervention in selecting and implementing an appropriate structure for FB algorithms. Furthermore, these algorithms, when implemented in device FBs, provide a solution at the fieldbus level, reducing data traffic between gateway and device, and speeding up the process of problem resolution. We also show some examples of our approach. The first is a neural network architecture change that allocates different types of neural networks in field devices without interrupting the fieldbus network operation. The second shows a multiagent architecture that implements the neural network change in a laboratory test process, where fault scenarios have been simulated.

Published in:

Industrial Electronics, IEEE Transactions on  (Volume:57 ,  Issue: 5 )