Abstract
In view of the increasing amount of information in the form of alarms, messages or also acoustic signals, the operators of systems are exposed to more workload and stress than ever before.We develop a concept for the reduction of alarm floods in industrial plants, in order to prevent the operators from being overwhelmed by this flood of information. The concept is based on two phases. On the one hand, a learning phase in which a causal model is learned and on the other hand an operating phase in which, with the help of the causal model, the root cause of the alarm sequence is diagnosed. For the causal model, a Bayesian network is used which maps the interrelations between the alarms. Based on this causal model the root cause of an alarm flood can be determined using inference. This not only helps the operator at work, but also increases the safety and speed of the repair. Additionally it saves money and reduces outage time. We implement, describe and evaluate the approach using a demonstrator of a manufacturing plant in the SmartFactoryOWL.
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Wunderlich, P., Niggemann, O. (2018). Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause. In: Niggemann, O., Schüller, P. (eds) IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency. Technologien für die intelligente Automation, vol 8. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57805-6_7
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DOI: https://doi.org/10.1007/978-3-662-57805-6_7
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