Interactive and Intelligent Root Cause Analysis in Manufacturing with
  Causal Bayesian Networks and Knowledge Graphs
Christoph Wehner Maximilian Kertel Judith Wewerka
Root Cause Analysis (RCA) in the manufacturing of electric vehicles is the
process of identifying fault causes. Traditionally, the RCA is conducted
manually, relying on process expert knowledge. Meanwhile, sensor networks
collect significant amounts of data in the manufacturing process. Using this
data for RCA makes it more efficient. However, purely data-driven methods like
Causal Bayesian Networks have problems scaling to large-scale, real-world
manufacturing processes due to the vast amount of potential cause-effect
relationships (CERs). Furthermore, purely data-driven methods have the
potential to leave out already known CERs or to learn spurious CERs. The paper
contributes by proposing an interactive and intelligent RCA tool that combines
expert knowledge of an electric vehicle manufacturing process and a data-driven
machine learning method. It uses reasoning over a large-scale Knowledge Graph
of the manufacturing process while learning a Causal Bayesian Network. In
addition, an Interactive User Interface enables a process expert to give
feedback to the root cause graph by adding and removing information to the
Knowledge Graph. The interactive and intelligent RCA tool reduces the learning
time of the Causal Bayesian Network while decreasing the number of spurious
CERs. Thus, the interactive and intelligent RCA tool closes the feedback loop
between expert and machine learning method.