A testbed for data fusion for engine diagnostics and prognostics

A key to producing reliable engine diagnostics and prognostics resides in fusion of multisensor data. It is believed that faults will manifest effects in a variety of sensors. By \´integration\´ (fusion) of information across sensors detections can be made of faults that are undetectable on just a single sensor. Data to support development of prognostic techniques is very rare. The development requires continuous collection of significant amounts of data to capture not only "normal" data but also capture potential fault event data well before the fault is detected by existing techniques, as well as capture data related to rare events. The collected data can be analyzed to develop processing tailored to new events and to continuously update algorithms so as to improve detection and classification performance and reduce false alarms. IAC in collaboration with the Air Force and the Army is developing a testbed to perform data collection and to develop fusion techniques for gas turbine engine health monitoring. The testbed and examples of its operation are presented here.