Planetary gear trains (PGTs) are widely used in many industrial applications from wind turbines to automobile transmissions due to their high power-to-weight ratio. Since PGT can be subjected to faults and prone to failure over time, a non-intrusive method of monitoring the condition of PGT is required. Vibration-based machine learning algorithms are mostly used in fault diagnosis and classification in PGT, but due to the epicyclic motion of gears, vibration signals from one gear can get neutralized or amplified by signals from another gear. In addition, identification of smaller cracks in the sun gear can be challenging when using vibration-based fault diagnosis alone due to cancelation effects from the planet pinions. In this paper, epicyclic drivetrain of a Chevy Volt hybrid car is considered to study two scenarios: (1) detecting microcracks (0.02 mm) and (2) identifying faults in sun gear while its signal is affected by cracks at the planetary gears. Four different cases: a healthy PGT, PGT with a sun crack, PGT with a planet crack, and a PGT with a sun and a planet crack, were considered and simulated using the MSC ADAMS software. The joint forces at the exterior ring gear were extracted, and a Blackman function was applied to conversion to frequency-domain values. Each of the frequency-domain amplitude values was converted to pixel values in a grayscale image, and the generated images were fed into a convolutional neural network (CNN) to train, validate, and test the datasets. The results indicated that the proposed grayscale 2D CNN algorithm has an accuracy of 92% for the test set.