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Sensor and actuator self-validation is a critical step in system control and fault diagnostics. If sensors do not work properly, one cannot rely on their outputs to further deduce system status. Similarly, faulty actuators will not satisfy system performance objectives and may cause disasters in feedback control systems. In this paper, a novel method to generate structured residuals for isolating sensor and actuator failures with the least sensitivity to model-plant-mismatch (MPM) and disturbances in multivariate dynamic systems is proposed. The proposed method includes two components. The first component is the generation of the primary residuals directly from noisy input and output measurements without identifying explicitly the model of a system under consideration. The primary residuals are generated such that they have the least sensitivity to any MPM and process disturbances, but have the highest sensitivity to faults in any sensors and/or actuators. The second component of the proposed scheme is the max-min design to transform the primary residuals into a set of structured residuals for fault isolation by improving the existing structured residual approach with maximized sensitivity (SRAMS) . Since one structured residual is made immune to a specified subset of faults, but very sensitive to other faults, any faulty sensors and/or actuators can be isolated by observing the structured residuals in accordance with a predetermined isolation logic. The proposed method has been verified for detection and isolation of faulty sensors and/or actuators in an experimental pilot plant.