A novel ontology-assisted inference platform in automotive troubleshooting tasks

Abstract

Recent intelligent systems as required for Industry 4.0 merge data from diverse domains and more gradually demand data to be combined with field knowledge. The convergence and scenarization of data permits for the high-level inferring required to create knowledge based on the data under consideration. In this study, a framework for an ontology-assisted multi-scenario inference platform is proposed to help some of the desirable platform qualities in automotive troubleshooting service involve message clarity, platform interoperability, and elegant maturing. This framework is constructed through the model with triple modes (Conception-Expression-Manipulation, CEM), which is a communication-based framework. This proposed framework applies a two-tier class with three performers and can combine and use multiple scenarios. There are several characteristics, including flexibility, interaction, and handily maintenance. The transformation of data is separated from one element of the platform and thus does not implicate several other elements. A field of employment can be easily decided by the utilization of prototypes and field-norm elements. This proposed framework is instantiated applying an instance study including data from the troubleshooting tasks of automotive system.