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-Condition monitoring is an important and challenging task actual for many areas of industry, medicine and economics. Nowadays it is necessary to provide on-line monitoring of the complex systems status, e.g. the steel production, in order to avoid faults, breakdowns or wrong diagnostics. In the present paper a novel machine learning method for the automated condition monitoring is presented. Neural Clouds (NC) is a novel data encapsulation method, which provides a confidence measure regarding classification of the complex system conditions. The presented adaptive algorithm requires only the data which corresponds to the normal system conditions, which is typically available. At the same time the fault related data acquisition is expensive and fault modeling is not always possible, especially in case one is dealing with steel production, power stations operation, human health condition or critical phenomena in financial markets. These real word applications are also presented in the paper.

A rolling mill process control system calculates the setup for the mill's actuators based on models of the technological process. Neural networks are applied as components of hybrid neuro/analytical process models. They are the key to fit the general physical models to the needs of the automation of a specific mill. Present applications include the calculation of the rolling force and strip temperature (hot and cold rolling); prediction of width-spread in the finishing mill; control of strip width shape; and control of the coiling temperature in a cooling train (hot rolling). The authors outline how significant benefits are achieved in rolling mill technology by using neural networks. The work presented here is the result of a close cooperation between Siemens Corporate Technology in Munich and the Industrial Projects and Technical Services Group in Erlangen.

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