  In industrial manufacturing, modern high-tech equipment delivers an
increasing volume of data, which exceeds the capacities of human observers.
Complex data formats like images make the detection of critical events
difficult and require pattern recognition, which is beyond the scope of
state-of-the-art process monitoring systems. Approaches that bridge the gap
between conventional statistical tools and novel machine learning (ML)
algorithms are required, but insufficiently studied. We propose a novel
framework for ML based indicators combining both concepts by two components:
pattern type and intensity. Conventional tools implement the intensity
component, while the pattern type accounts for error modes and tailors the
indicator to the production environment. In a case-study from semiconductor
industry, our framework goes beyond conventional process control and achieves
high quality experimental results. Thus, the suggested concept contributes to
the integration of ML in real-world process monitoring problems and paves the
way to automated decision support in manufacturing.
