  In today's rapidly evolving landscape of automation and manufacturing
systems, the efficient resolution of productivity losses is paramount. This
study introduces a data-driven ensemble approach, utilizing the cyclic
multivariate time series data from binary sensors and signals from Programmable
Logic Controllers (PLCs) within these systems. The objective is to
automatically analyze productivity losses per cycle and pinpoint their root
causes by assigning the loss to a system element. The ensemble approach
introduced in this publication integrates various methods, including
information theory and machine learning behavior models, to provide a robust
analysis for each production cycle. To expedite the resolution of productivity
losses and ensure short response times, stream processing becomes a necessity.
Addressing this, the approach is implemented as data-stream analysis and can be
transferred to batch processing, seamlessly integrating into existing systems
without the need for extensive historical data analysis. This method has two
positive effects. Firstly, the result of the analysis ensures that the period
of lower productivity is reduced by identifying the likely root cause of the
productivity loss. Secondly, these results are more reliable due to the
ensemble approach and therefore avoid dependency on technical experts. The
approach is validated using a semi-automated welding manufacturing system, an
injection molding automation system, and a synthetically generated test PLC
dataset. The results demonstrate the method's efficacy in offering a
data-driven understanding of process behavior and mark an advancement in
autonomous manufacturing system analysis.
