Информационные технологии интеллектуальной поддержки принятия решений, Информационные технологии интеллектуальной поддержки принятия решений 2018

Размер шрифта: 
An Artificial Neural Network for Automated Fault Detection
Evgenia Rusak, Julian Bitterwolf, Sebastian Reiter, Alexander Viehl, Oliver Bringmann

Изменена: 2018-06-20


Intelligent and interconnected cyber physical systems are a key enabler for future cost-efficient, automated and flexible industrial production systems. Predictive maintenance and condition monitoring are important techniques in order to reduce costs associated with unnecessary maintenance or premature breakdowns. In this paper, we propose techniques from supervised learning for automated malfunctioning detection. For that purpose, we train an artificial neural network on time series data representing the internal system behavior. We present experimental results from an industrial motor control system. We use a digital twin of the electronic component that models the relevant features of the physical system. The obtained information can be used during the runtime of technical systems and installations for a criticality analysis and the subsequent selection of measures.

Ключевые слова

neural networks; fault detection; industrial production systems


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