SCCH presented Predictive Analytics
Finding the "Early Warning Point"
The Automotive Cluster Upper Austria (http://www.automobil-cluster.at) organized the Innovation Workshop at MAGNA in Graz. The experts from the research focus Data Analysis Systems (DAS) showed how predictive maintenance works and how the "Early Warning Point" can be found.
Manage huge amounts of data and recognize relationships
Huge data streams (for example machine data, process data, etc.) from different, heterogeneous data sources must be combined and analyzed to provide a meaningful basis for making decisions and recommendations. SCCH presented the methods for implementing predictive analytics and predictive maintenance.
Where is the Early Warning Point?
Through the use of data mining and machine learning methods fault prediction models are created to find the "early warning point", which is enabling predictive maintenance strategies. The key is the combination of domain expertise and data-based fault prediction models.
The range of applications for these methods ranges from the process industry and production, through the energy management and the maintenance of machinery and equipment.