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Fault diagnosis is an important area in nuclear power industry for effective and continuous operation of power plants. Fault diagnosis approaches depend critically on the sensors that measure important process variables. Allocation of these sensors determines the effectiveness of fault diagnostic methods. However, the emphasis of most approaches is primarily on the procedure to perform fault detection and isolation (FDI) given a set of sensors. Little attention has been given to actual allocation of the sensors for achieving efficient FDI performance. This paper presents a graph-based approach as a solution for optimization of sensor selection to ensure fault observability, as well as fault resolution to a maximum possible extent. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships among the measurements and to characterize by a data hyper-plane. Fault directions for the different fault scenarios are obtained using singular value decomposition of the prediction errors, and fault isolation is then accomplished from new projections on these fault directions. Results of the helical coil steam generator (HCSG) system of the International Reactor Innovative and Secure (IRIS) nuclear reactor demonstrate the proposed FDI approach with optimized sensor selection, and its future application to large industrial systems.