[ ] SIENA

Stimulation Initiative for European Neural Applications

Esprit Project 9811

Case Studies of Successful Applications

Alarm Identification with SENECA

Company background

No further information available

The problem

In a continuous casting facility molten steel is poured from a melting pot into a tundish and from there into a watercooled mold where it solidifies. The solid steel is then cut into pieces of a fixed lenght for further treatment in a rolling mill. Due to the cooling of within the mold the steel develops a solid shell which surrounds a still molten core when it is withdrawn from the mold. From time to time the formation of the solid shell is disturbed so that a rupture of the shell occures, the rupture leaves the mold and liquid stell leaks into the facilities. This causes long shutdown times an enormous costs.

Neural network application

To detect the temperature signal pattern which is characteristic for a rupture a neural network based system has been developed. It consists of multi-layer-perceptron networks for each sensor which share their weights among each other and generate a response whether the network detected a rupture or not. The system was trained with data collected from 262 casting processes which have occured during the operation of the casting facility. Within this data were 86 real and 171 false alarms detected by a conventional algorithm. A selected training set of real and false alarms has been used to train the neural network.

Benefits

Over a period of several months the system has been tested parallel to the conventional algorithm. All real alarms have been detected by both systems. However, the alarms from the neural detection system came up to 14 seconds earlier than those of the conventional system. In online operation, the neural detection system's false alarm rate is considerably lower in comparison to the conventional system.

Generalization

The ability of neural networks to detect complex patterns in noisy signals makes them a valuable tool in almost any fault detection system and helps increasing the production's quality standards. The described application is one example for a neural network based system which has been trained on historical data and which can improve continuously by learning from experiences gathered during online operation.

Contact person

Stefan Fuhrmann - Zentrum für Neuroinformatik GmbH. Universitätsstraße 160, D-44801 Bochum. Phone: +49 234 9787-51, Fax: +49 234 9787-77.