Localizing Faults in Digital Twin Models by Using Time Series Classification Techniques

To provide its services, a digital twin system relies on models whose behavior adequately simulates/mimics that of the twinned system. Deviations between the behaviors necessitate (user) intervention to realign the model and twinned system. We can intervene in the model (e.g. reinitialization, reparametrization) or in the twinned system (e.g. physical adjustment, replacement of parts). In either case, the taken action depends on the cause of the deviation. One way to find the cause is to use a classifier on the digital twin system's data. In a digital twin system, the data is generally a time-series of states, inputs and outputs. In this paper, we apply a state-of-the-art time series classification technique to detect and classify faults in a digital twin system of a scale-model gantry crane. We find that a classifier trained on simulation data yields adequate performance when applied on to the real system.