Business Intelligence Architecture for Process Quality Monitoring with BDD

The Industry 4.0 vision aims for high-quality and flexible production processes that are automated with Cyber-Physical Production Systems (CPPSs) to address changes in demand and the environment. Process Quality Monitoring (PQM) shall ensure the desired process quality and reacting with low delay to deviations towards undesired process quality. CPPS limitations to monitor conditions in a multi-domain environment may be mended with Digital Twin (DT) functions to address emerging undesired behavior. However, it remains unclear how to elicit the tacit and scattered knowledge required to monitor desired and undesired states of the physical system for effective PQM. This paper introduces the approach Process Quality Monitoring with Behavior-Driven Development (PQM+BDD) to (i) represent the business intelligence architecture, i.e., cause-effect knowledge and data, required to design a PQM solution variant and (ii) to design the knowledge model for Behavior-Driven Development scenarios as input to specify and validate a PQM solution by augmenting a CPPS with Digital Twin functions. We evaluated PQM+BDD in a laboratory-scale production system to explore its feasibility, effectiveness, and efficiency. The results indicate PQM+BDD to be feasible, effective, and efficient in comparison to a best practice method.

Talk language: English
Level: Scientific
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Company:
Technische Universität Wien

Prof. Dr. Stefan Biffl