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Digital twin technology has found wide applications in the aerospace industry, for fleet monitoring, diagnostics, predictive maintenance, and manufacturing. Sensing the aero-engine in a test environment is challenging, due to issues with noisy and failed sensors, introduced by extreme conditions. Downstream diagnostics and prognostics require that key sensed values are available for the duration of the test for both real-time and off-line analysis. Virtual sensors that mimic the behavior of the physical sensors provide a resilient solution in the presence of such failures. This paper describes virtual sensors designed using a low-cost digital twin platform with off-the-shelf analytical software components and libraries. The platform is a no-code environment, that permits users to rapidly experiment with different analytical models and configurations. We show that virtual sensors can emulate the behavior of the physical sensor(s) in the event of multiple physical sensor failures with high accuracy, the design of which is facilitated by the Digital Twin platform. From a process standpoint, the Digital Twin results in several advantages to the organization including the breaking down of departmental data silos, reevaluation of key assumptions regarding system design, and the standardization of monitoring process. The digital twin approach is seen to be a catalyst for reengineering the design and monitoring lifecycle for industrial organizations.