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A novel Mahalanobis Taguchi System (MTS) based fault detection, isolation, and prognostics scheme is presented. The proposed scheme fuses data from multiple sensors into a single system level performance metric using Mahalanobis Distance (MD), and generates fault clusters based on MD values. MD thresholds derived from the clustering analysis are used for fault detection and isolation. When a fault is detected, the prognostics scheme, which monitors the progression of the MD values over time, is initiated. Then, using a linear approximation, time to failure is estimated. The performance of the scheme has been validated via experiments performed on a mono-block centrifugal water pump testbed. The pump has been instrumented with vibration, pressure, temperature, and flow sensors; and experiments involving healthy and various types of faulty operating conditions have been performed. The experiments show that the proposed approach renders satisfactory results for centrifugal water pump fault detection, isolation, and prognostics. Overall, the proposed solution provides a reliable multivariate analysis and real-time decision making tool that 1) fuses data from multiple sensors into a single system level performance metric; 2) extends MTS by providing a single tool for fault detection, isolation, and prognosis, eliminating the need to develop each separately; and 3) offers a systematic way to determine the key parameters, thus reducing analysis overhead. In addition, the MTS-based scheme is process independent, and can easily be implemented on wireless motes1, and deployed for real-time monitoring, diagnostics, and prognostics in a wide variety of industrial environments.