The term Industry 4.0 (the smart factory) has become a buzzword. Often huge data streams (e.g., machine data, process data, quality data) from diverse, heterogeneous data sources must be linked and analyzed in order to provide a useful basis for decision support and recommended
actions for humans. The application spectrum ranges from the process industry and production, to energy management to the manufacture and maintenance of machines and plants. Predictive maintenance is another important buzzword. If an industrial production system suffers unforeseen
disruption of a machine and therefore downtime, then this is a worst-case scenario: production is delayed and enormous costs can ensue.
Modern control systems are incapable of evaluating their own state so as to derive relevant information for the maintenance crew. The vision of machine diagnosis and prognosis is to close this gap. The goal is to predict
a time (warning) when corresponding service measures can avoid possible damage or downtime. This improves the state of a component or machine and production continues according to plan. Data mining and machine learning methods enable us to create fault prediction models in order to find this early warning point and thus plan predictive maintenance strategies.
The key is the combination of expert knowledge and data-based fault prediction models. This increases plant availability with reduced use of resources. The use of predictive maintenance strategies is
promising in many areas:
* Increased plant availability because fault prediction
promotes early detection of damages and reduced
*Reduced material and energy costs because maintenance
is not bound to predefined schedules but
instead conducted as needed
*Improved planability of maintenance via state monitoring
*Heightened operational security by avoidance of
Navigate to www.scch.at/en/das-projects for additional
information about Predictive Analytics und Predictive Maintenance.