Fuzzy entropy used for predictive analytics

Process interruptions in (very) large production systems are difficult to deal with. Modern processes are highly automated; data is collected with sensor technology that forms a big data context and offers challenges to identify coming failures from the very large sets of data. Feature selection is intended to reduce the complexity of identifying cases with high possibility of failure by excluding numerous factors in the process systems. We use fuzzy entropy as the basis of a feature selection method and we show how the outcome of feature selection can be utilized to further failure prediction steps.