Describes a robust sensor fault diagnosis algorithm for a class of nonlinear dynamic systems. Specifically, the paper uses adaptive techniques to estimate the unknown constant sensor bias in the presence of system modeling uncertainties and sensor noise. The robustness, sensitivity and stability of the adaptive fault diagnosis architecture are rigorously established. A simulation example to illustrate the use of the proposed fault diagnosis architecture to diagnose bias in an automotive Universal Exhaust Gas Oxygen sensor is presented.