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SENECA preview
| Introduction |
From Application of Artificial Neural network Systems in the steel industry.
SENECA uses several Multi-Layer-Perceptrons for the 16 thermocouples of the upper sensor rows on the front and on the rear side of the mould. The network weights can be used by multiple MLPs (weight sharing). The networks observe and classify the temperature profiles that have been measured during the past one minute of the caster operation.
During off-line training the temperature profiles are presented to the net together with the respect classification as the target value (alarm/no alarm). The subjective classification has been done by experts on the basis of inspections of the slab surface as well as on the basis of evaluations of the temperature signals.
Comparison of classification performance of conventionnal and neural network based breakout prediction algorithm of SENECA:
+-------------------+---------------+--------+
| System | Conventionnal | SENECA |
+-------------------+---------------+--------+
| Sticker | 86 | 91 | Alarm was justified
| Misclassification | 171 | 25 | Alarm was not justified
| Breakout | 5 | 0 | No alarm has been generated
| Normal operation | 0 | 146 | No alarms
+-------------------+---------------+--------+
Off-line evaluation of the system performance based on the available data already showed that SENECA was able to correctly detect all real alarms. The classification ability was independant of the steel grades. Moreover, SENECA was able to identify five ruptures which have not been found by the conventionnal system and led to a real breakout. the rate of false alarms generated by the detection mechanism could be lowered to about 20% of the conventionnal algorithm.
Over a periodof several months the system has been tested in parallel to the conventionnal algorithm. The results of the off-line-evaluation have been fully confirmed. All real alarms have been detected by both systems. However, the alarms from the neural detection system came up to 14 seconds earlier then those of the conventionnal system. in online operation, SENECA's flase alarm rate is about 25% compared to the conventionnal system. this can be improved by using the collected data for additionnal training.
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