Three-phase AC motor drives play a key role in several applications, including energy conversion and automotive. Mainly, three-phase AC motor drives operate as closed loop control systems, where accurate feedback measurement sent by the current sensors is crucial to guarantee the good operation of the system. However, current sensors are potentially subject to several malfunctions that significantly affect the performance of the drive system. Accordingly, this paper proposes an efficient method for current sensor fault detection, and identification in three-phase AC motor drive system using a 2D Convolutional Neural Network (CNN). The proposed approach does need any additional extra-hardware components, since it uses only the signals already sent by the motor drive closed loop control. Indeed, it utilizes the 2D trajectory graph of the normalized motor current vector as input to a novel CNN Autoencoder model, which is introduced for feature extraction and classification. The efficiency and generalization capabilities of the proposed CNN autoencoder (PCAE) are benchmarked against a standard CNN model and conventional CNN autoencoders. The lightweight architecture of the PCAE enables its real-time implementation on a Raspberry pi 4 with a 750w experimental setup induction motor. The experimental results highlight that the proposed PCAE model can effectively detect and classify ten types of current sensor faults, in addition to distinguishing the healthy operation case. Moreover, the proposed approach achieves superior accuracy (99%), compared with conventional CNN (95%) and standard CNN-Autoencoder (96%) models.