Nowadays, gas turbines (GTs) are equipped with an increasing number of sensors, of which the acquired data are used for monitoring and diagnostic purposes. Therefore, anomaly detection in sensor time series is a crucial aspect for raw data cleaning, in order to identify accurate and reliable data. To this purpose, a novel methodology based on Bayesian hierarchical models (BHMs) is proposed in this paper. The final aim is the exploitation of information held by a pool of observations from redundant sensors as knowledge base to generate statistically consistent measurements according to input data. In this manner, it is possible to simulate a “virtual” healthy sensor, also known as digital twin, to be used for sensor fault identification. The capability of the novel methodology based on BHM is assessed by using field data with two types of implanted faults, i.e., spikes and bias faults. The analyses consider different numbers of faulty sensors within the pool and different fault magnitudes. In this manner, different levels of fault severity are investigated. The results demonstrate that the new approach is successful in most fault scenarios for both spike and bias faults and provide guidelines to tune the detection criterion based on the morphology of the available data.