Discret2Di -- Deep Learning based Discretization for Model-based Diagnosis
This addresses the problem of high modeling costs for engineers in technical diagnosis, though it appears incremental as it builds on existing logical calculi.
The paper tackles the challenge of automating logical rule learning for consistency-based diagnosis in dynamic multi-modal time series by introducing Discret2Di, a method that combines machine learning from time series and symbolic domains to reduce modeling efforts.
Consistency-based diagnosis is an established approach to diagnose technical applications, but suffers from significant modeling efforts, especially for dynamic multi-modal time series. Machine learning seems to be an obvious solution, which becomes less obvious when looking at details: Which notion of consistency can be used? If logical calculi are still to be used, how can dynamic time series be transferred into the discrete world? This paper presents the methodology Discret2Di for automated learning of logical expressions for consistency-based diagnosis. While these logical calculi have advantages by providing a clear notion of consistency, they have the key problem of relying on a discretization of the dynamic system. The solution presented combines machine learning from both the time series and the symbolic domain to automate the learning of logical rules for consistency-based diagnosis.