ArXiv Preprint
Vehicles are complex Cyber Physical Systems (CPS) that operate in a variety
of environments, and the likelihood of failure of one or more subsystems, such
as the engine, transmission, brakes, and fuel, can result in unscheduled
downtime and incur high maintenance or repair costs. In order to prevent these
issues, it is crucial to continuously monitor the health of various subsystems
and identify abnormal sensor channel behavior. Data-driven Digital Twin (DT)
systems are capable of such a task. Current DT technologies utilize various
Deep Learning (DL) techniques that are constrained by the lack of justification
or explanation for their predictions. This inability of these opaque systems
can influence decision-making and raises user trust concerns. This paper
presents a solution to this issue, where the TwinExplainer system, with its
three-layered architectural pipeline, explains the predictions of an automotive
DT. Such a system can assist automotive stakeholders in understanding the
global scale of the sensor channels and how they contribute towards generic DT
predictions. TwinExplainer can also visualize explanations for both normal and
abnormal local predictions computed by the DT.
Subash Neupane, Ivan A. Fernandez, Wilson Patterson, Sudip Mittal, Milan Parmar, Shahram Rahimi
2023-02-01
