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Author Reuter, M. ♦ Elzer, P. ♦ Berger, A.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1995
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations
Subject Keyword Neural networks ♦ Education ♦ Process control ♦ Fault diagnosis ♦ Monitoring ♦ Power measurement ♦ Power generation ♦ Mechanical factors ♦ Alarm systems ♦ History
Abstract The application of neural networks in supervision and control of technical processes requires not only their abilities to classify process states and identify possible faulty or dangerous ones but also the possibility to monitor changes of process variables over time in order to predict eventually developing dangerous states. However, the traditional methods of teaching neural nets have shown that nets did nor provide for this later capability, when they were trained by a data set in which all evolutionary states from which faulty or dangerous situations can arise are involved. The following paper presents a method of teaching neural nets by means of sequences of sets of process values which converges towards process states that are known to be faulty or dangerous. The method had originally been developed with the aim of improving the quality and speed of the detection of static process states, but can also be applied to the early detection of changes in the process that may lead to dangerous states. Until now, measurements with a simulated coal-fired power plant have shown very promising properties of the proposed mechanism. So a neural net ruled supporting and warning system has been conditioned by data sets representing the plant when all parts are working at their operation points and by some sets representing clearly presenting faulty states to create an undressed basis structure/concept of the neural net classificator. This basis structure was successively sensitized by teaching evolutionary states of the faulty states which were younger and younger in its development history. The test results showed that now even slightly from each other differing sensor patterns and/or evolutionary stales of arising faults can be detected. Especially the net can separate even faulty states when 2 of 157 data of the sensor representation of the plants working condition changed about 2% only.
Description Author affiliation: Inst. for Process & Production Control Tech., Tech. Univ. Clausthal, Germany (Reuter, M.; Elzer, P.; Berger, A.)
ISBN 0780325591
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1995-10-22
Publisher Place Canada
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 733.07 kB
Page Count 6
Starting Page 1681
Ending Page 1686


Source: IEEE Xplore Digital Library