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The paper presents an effective and efficient method for sensor fault detection and identification within a large group of sensors based upon hierarchical cluster analysis. Fingerprints of the hierarchical clustering dendrograms are found for normal operation using normalized data, and sensor faults are detected through cluster changes occurring in the dendrogram. The proposed strategy is built into a user-friendly graphical interface, which is applied to a sub-15MW industrial gas turbine. It is shown, through use of real-time operational data, that inoperation sensor faults can be detected and identified by the hierarchical clustering-based graphical user interface.