  Operational knowledge is one of the most valuable assets in a company, as it
provides a strategic advantage over competitors and ensures steady and optimal
operation in machines. An (interactive) assessment system on the shop floor can
optimize the process and reduce stopovers because it can provide constant
valuable information regarding the machine condition to the operators. However,
formalizing operational (tacit) knowledge to explicit knowledge is not an easy
task. This transformation considers modeling expert knowledge, quantification
of knowledge uncertainty, and validation of the acquired knowledge. This study
proposes a novel approach for production assessment using a knowledge transfer
framework and evidence theory to address the aforementioned challenges. The
main contribution of this paper is a methodology for the formalization of tacit
knowledge based on an extended failure mode and effect analysis for knowledge
extraction, as well as the use of evidence theory for the uncertainty
definition of knowledge. Moreover, this approach uses primitive recursive
functions for knowledge modeling and proposes a validation strategy of the
knowledge using machine data. These elements are integrated into an interactive
recommendation system hosted on a backend that uses HoloLens as a visual
interface. We demonstrate this approach using an industrial setup: a laboratory
bulk good system. The results yield interesting insights, including the
knowledge validation, uncertainty behavior of knowledge, and interactive
troubleshooting for the machine operator.
