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Several factors must be considered for robotic task execution in the presence of a fault, including: detection, identification, and accommodation for the fault. In this paper, a prediction error based dead-zone residual function and nonlinear observers are used to detect and identify a class of actuator faults. Advantages of the proposed fault detection and identification methods are that they are based on the nonlinear dynamic model of a robot manipulator (and hence, can be extended to a number of general Euler Lagrange systems), they do not require acceleration measurements, and they are independent from the controller. A Lyapunov-based analysis is provided to prove that the developed fault observer converges to the actual fault.