Vibration monitoring is an important practice throughout regular operation of gas turbine power systems and, even more so, during characterization tests. Vibration monitoring relies on accurate and reliable sensor readings. To obtain accurate readings, sensors are placed such that the signal is maximized. In the case of characterization tests, strain gauges are placed at the location of vibration modes on blades inside the gas turbine. Due to the prevailing harsh environment, these sensors have a limited life and decaying accuracy, both of which impair vibration assessment. At the same time bandwidth limitations may restrict data transmission, which in turn limits the number of sensors that can be used for assessment. Knowing the sensor status (normal or faulty), and more importantly, knowing the true vibration level of the system all the time is essential for successful gas turbine vibration monitoring. This paper investigates a dynamic sensor validation and system health reasoning scheme that addresses the issues outlined above by considering only the information required to reliably assess system health status. In particular, if abnormal system health is suspected or if the primary sensor is determined to be faulted, information from available “sibling” sensors is dynamically integrated. A confidence expresses the complex interactions of sensor health and system health, their reliabilities, conflicting information, and what the health assessment is. Effectiveness of the scheme in achieving accurate and reliable vibration evaluation is then demonstrated using a combination of simulated data and a small sample of a real-world application data where the vibration of compressor blades during a real time characterization test of a new gas turbine power system is monitored.