Although the viability of data-driven Predictive Maintenance (PdM) solutions is often thwarted bythe lack of available faulty data, leading to challenged training for deep learning models. Thispaper presents a Physics-Informed Domain Adaptation (PIDA) approach, which combines a high?precision Digital Twin model for a direct current (DC) motor and a one-dimensional ConvolutionalNeural Network (1D-CNN), for successful Sim-to-Real fault diagnosis in a data-scarce setting.This Digital Twin representation is established using a system of coupled differential equations,which model electromechanical phenomena, with simulated system parameters set to R= 2.0 Ωand B = 0.001 Nm· s/rad for healthy cases. Fault conditions can be simulated by parametervariations, such as R = 0.5 Ω (short circuit in stator) and B = 0.05 (bearing friction fault). Theconcept of Domain Randomization is applied by adding a zero-mean Gaussian noise process 𝑁 (0,0.1) that allows overcoming differences between simulated and actual signals coming fromphysical sensors. Using the aforementioned hybrid approach, which was trained on 100%simulated data and 50% vibration data from real cases, a perfect accuracy (100%) with precision= 1.00 and a perfect recall measure (100%) was obtained in classifying the CWRU-bearing testdata set.