Optimal Sensor Selection for Active Fault Diagnosis Using Test Information Criteria

Abstract

A method for the optimal selection of sensors in active fault detection and isolation tests is presented. The usefulness of test information is improved using parametric sensitivities derived by a digital twin of the system. In this model-based approach, inputs are manipulated to maximize output sensitivities with respect to faults and minimize the joint confidence region between faults and other uncertain parameters. Two criteria are used to select optimal sensors based on information theory. The optimal selection is dependent on measurement precision, as shown in a case study of a virtual three-tank system subject to multiple faults.