Nowadays, robots become increasingly more autonomous, which gives more importance to the Fault Detection and Isolation (FDI) task. In this article, major existing faults classification is specified. Faults are classified with respect to their time dependency, their source and their effect on system model. After that, mobile robotics-suitable FDI methods are classified into four main categories: material redundancy based, knowledge based, data based and model based approaches. Then, Extended Kalman Filter (EKF) and Multiple model Adaptive Estimation (MMAE) are explained and applied in a simulation to detect and isolate efficiently four wheel block faults, after studying briefly how wheel block faults affect the robot model. The average detection and isolation rate in the presented simulation is in order of 90.