Bench development tests of new turbofan engines make an important use of sensor measurements to help engineers understand the behavior of new components design. Such tests are expensive and may be completely compromised if any measurement is missing. One of the big challenges about development tests is to minimize the number of sensors as each measurement has a non-negligible cost. However, if we suppress most of the existing redundancy it becomes very important to ensure a nominal working of the surviving sensors. On another hand, development tests are aimed to excite the systems in very specific configurations, often at the edge of the normal operational range, so very specific and original observations are frequent; thus testing if the sensor behaves according to specification is a challenge. Some sensor failures such as harness intermittencies are easy to detect because the fault pattern is specific, but a drift, a bias or even a trend caused by regulation and correction are not easy to highlight. To compensate the context variation, we model each sensor measurement, some models use only context dependencies given by nearby or redundant information and others make use of the temporal relations to consider the time dependency and continuity. The sensor model does not need to be very precise and more specifically it may be limited to specific regimes chosen among the m o s t recurrent configurations, hence improving local robustness. An autoadaptive clusterisation algorithm calibrated on the fly, identifies such recurrent configurations. The sensor models may be updated on line and the quality of each sensor is finally observed by analyzing the evolution of its model parameters or its frequency response for time dependent models. Clearly, the resulting model may be polluted by dependent and potentially faulty measurements, especially on controlled systems. Anyway, from these computations, clues to detect the specific faulty acquisition chains among a set of measurements are given and the methodology proposes an algorithmic correction scheme. The fault diagnostic is implemented in two steps: the detection of a problem, then its localization. The faulty measurement may be replaced by an estimation that corrects the physical observation according to past and validated measurements.