This paper presents an early version of the primary results on fault detection in multilevel inverters through artificial intelligence techniques. Multilevel inverters, while advantageous in terms of efficiency and waveform quality, are inherently prone to failures due to their large number of semiconductor components. Faults in switching devices, if not identified in time, are seen to cause severe instability and device breakdown. The original study assessed four deep learning (DL) models for reference voltage prediction and subsequent fault identification. In this conference version, the discussion is limited to the convolutional neural network (CNN) method. Localized temporal and spatial features in voltage signals are extracted by CNNs, by which precise recognition of abnormal switching behavior is achieved. These preliminary results confirm that the CNN approach is promising and are used as a basis for extended journal research, which is planned to include broader comparisons and comprehensive validation. Experimental investigations also confirm that CNN-based methods achieve reliable fault detection in real time.