Building a smart database for predictive maintenance in already implemented manufacturing systems

Predictive analytics methods have become increasingly important in Manufacturing Organization in the context of Smart Maintenance. Standardized process models for data mining already known to search existing data stocks for patterns, trends and correlations. Sensors are progressively implemented in production machines to create a database for data mining processes. But the risk of Big Data, thus the risk of low quality data is probably high. For an economic consideration, the amount of investment in new measurement technology and infrastructure should be assessed. Organizations are confronted with the challenge of how much they have to invest to obtain a meaningful database. For this reason, it is important to research which existing approaches support the development of a sufficient database for predictive maintenance in manufacturing systems and provide a methodical framework.