This study proposes an innovative fault diagnosis algorithm developed for the 4WIS4WID vehicle system, aiming to overcome fault location and identification challenges. The 4WIS4WID system utilizes hardware redundancy and fault-tolerant control to maintain vehicle operation even when a fault occurs. However, because the number of degrees of freedom of the vehicle is less than the number of faults, it is impossible to distinguish analytically between faults. The proposed algorithm employs residual analysis to address this limitation, capturing the differences between predicted vehicle behavior and actual sensor data. The residuals are converted into a three-channel image using a 2-D histogram and a logical function to form a single fault image. A convolutional neural network (CNN) learns these fault images to detect the occurrence of a fault and accurately determine its location. The Recursive Least Square (RLS) algorithm is utilized to classify fault types. This method identifies the fault's size and type, and the vehicle's output in which the fault occurred is estimated. The proposed algorithm's fault diagnosis and classification performance are verified through CarMaker-based simulation. This systematic approach to fault diagnosis enhances vehicle safety and reliability in intelligent vehicle applications.