Using a 2-D Convolutional Neural Network (CNN) method for fault diagnosis, appropriate fault diagnosers are composed, for a type of Automated Guided Vehicles (AGVs), having four categories of actuators and three categories of sensors. The actuators and the sensors are modeled as Discrete Event Systems (DESs) in the Ramadge-Wonham (RW) framework. The diagnoser consists of an online level for fault detection and isolation and an offline level for the CNN training. The model of the presence and alter of faults, in AGV’s actuators and sensors, is expressed in the form of appropriate two-state automata that incorporate corresponding events produced by the fault diagnosers. A set of safety rules, considering the eventual presence of faults, will be introduced. The safety rules will be realized in the form of supervisor automata. The marked behavior of the controlled automata of the actuators and sensors will be provided to validate the satisfactory performance of the proposed method.