  Since high data volume and complex data formats delivered in modern high-end
production environments go beyond the scope of classical process control
systems, more advanced tools involving machine learning are required to
reliably recognize failure patterns. However, currently, such systems lack a
general setup and are only available as application-specific solutions. We
propose a process control framework entitled Health Factor for Process Control
(HFPC) to bridge the gap between conventional statistical tools and novel
machine learning (ML) algorithms. HFPC comprises two main concepts: (a) pattern
type to account for qualitative characteristics (error patterns) and (b)
intensity to quantify the level of a deviation. While the system retains large
model generality, allowing a broad scope of potential application areas, we
demonstrate its favorable mathematical properties in a theoretical analysis. In
a case study from the semiconductor industry, we underline that (a) our
framework is of practical relevance and goes beyond conventional process
control, and (b) achieves high-quality experimental results. We conclude that
our work contributes to the integration of ML in real-world process control and
paves the way to automated decision support in manufacturing.
