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Iterative learning scheme is proposed with the aim to achieve perfect tracking of a prescribed reference trajectory for systems that operate repetitively. In this paper, a sensor fault estimation framework is proposed for nonlinear repetitive system. First, the problem of sensor fault estimation is converted to actuator fault estimation via state redefinition. Afterward, state observer is designed for state reconstruction while iterative learning law is presented for fault estimation. Thus, the information in the previous period can be utilized to improve the fault estimating performance in current iterative trial. Avoiding the uncertainty caused by the norm optimal theory, the uniform convergence of error extended system is guaranteed by asymptotic stability and optimal function. Finally, the efficiency and merits of the proposed scheme are illustrated by numerical examples.