Using data mining techniques for development expert systems equipped with learning capabilities for use in automated industrial plants

Authors:
Alexandre Acácio De Andrade;Sergio Luiz Pereira;Eduardo Mario Dias;Caio Fernando Fontana
Affiliations:
GAESI Group, Electric Power and Automation Engineering Department, Polytechnic School at Sáo Paulo University, São Paulo, Brazil;GAESI Group, Electric Power and Automation Engineering Department, Polytechnic School at Sáo Paulo University, São Paulo, Brazil;GAESI Group, Electric Power and Automation Engineering Department, Polytechnic School at Sáo Paulo University, São Paulo, Brazil;GAESI Group, Electric Power and Automation Engineering Department, Polytechnic School at Sáo Paulo University, São Paulo, Brazil
Venue:
WSEAS Transactions on Systems and Control
Year:
2010

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Abstract

The use of expert systems gained importance with the growing amount of data that the current plants generate automated, in this paper, the development and tests of a specialist system based on data mining and that possesses the learning capacity is presented. The referred system was termed as NESISES (Neural System of Integration for Supervisory and Expert Systems). It operates in real time with industrial automation supervisory systems and whose aim is to minimize the frequent knowledge engineering procedures for constantly updating the base of knowledge of an Expert System (ES). NESISES was validated in both the laboratory as well as in an automation plant.