In this paper, the issue of iterative learning fault diagnosis (ILFD) and fault tolerant control (FTC) is studied for stochastic repetitive systems with Brownian motion. Different from existing fault diagnosis (FD) methods, a state/fault simultaneous estimation observer based on iterative learning method is designed. The convergence condition of the ILFD algorithm is given. By employing the fault estimation information, the FTC algorithm is proposed to compensate for the fault effect on the system and to keep the stochastic input-to-state stability of the control system. Finally, the simulation results of an induction motor system and a single-link robotic flexible manipulator system are given to show that the proposed method is validated.