Abstract: A method and system is provided for identifying in-range sensor faults in a gas turbine engine, by observing the tracked component qualities in an embedded model and recognizing anomalous patterns of quality changes corresponding to sensor errors. An embedded model of the engine is employed to estimate sensed parameters such as rotor speeds, temperatures and pressures, as well as other parameters that are computed based on input parameters. Each major rotating component of the engine, including the fan, compressor, combustor, turbines, ducts and nozzle is individually modeled. Sensor failures are detected by identifying anomalous patterns in component quality parameters. A library of anomalous patterns is provided for comparing quality parameters generated by a tracking filter with the library of anomalous patterns. If a pattern is matched, a sensor may be eliminated from the tracking filter and the estimated model parameter used to avoid corrupting the model quality parameters.

Abstract: A method of estimating quality parameters for a plurality of engine components of a gas turbine engine is provided. The engine components have at least one sensor responsive to the engine component operation. The method includes providing an engine model having virtual sensor values and quality parameters corresponding to the plurality of sensors of the engine components; comparing the virtual sensor values to actual sensor values of the plurality of sensors of the engine components to determine the difference between the actual and virtual sensor values; amplifying the difference by a predetermined gain; generating a plurality of quality parameter deltas in response to the sensed difference; iteratively updating the embedded engine model by inputting a predetermined portion of the generated quality parameter deltas into the embedded engine model; adjusting the embedded engine model for engine operating conditions; and recalculating the virtual sensor values.

Abstract: A method for predicting bearing failure of a differential bearing including an inner race, an outer race, and a plurality of rolling elements positioned between the inner and outer race. The method includes coupling an accelerometer to the differential bearing, generating a bearing performance model, receiving a signal from the accelerometer, and comparing the accelerometer signal to the bearing performance model to predict a differential bearing failure.

Abstract: A method for predicting bearing failure of a differential bearing including an inner race, an outer race, and a plurality of rolling elements positioned between the inner and outer race, the method includes coupling a measuring apparatus comprising at least one of a strain gage and an accelerometer to the differential bearing, coupling a cable to the measuring apparatus, wherein the cable is adapted for passage through a rotating component, and coupling a transmitter to the cable, wherein the transmitter is configured to transmit a plurality of signals from the measuring apparatus to a remote location to facilitate predicting a failure of the differential bearing.

Abstract: A method for predicting bearing failure of a differential bearing including an inner race, an outer race, and a plurality of rolling elements positioned between the inner and outer race. The method includes coupling a strain gage to the differential bearing, generating a bearing performance model, receiving a signal from the strain gage, and comparing the strain gage signal to the bearing performance model to predict a differential bearing failure.

Abstract: A method for monitoring performance of a gas turbine engine includes, during acceleration of an engine, accumulating regulator indices from an engine controller and times accumulated on acceleration regulators. At least one engine sensor value is also monitored after reaching a steady state takeoff power. The regulator indices, accumulated times, and the one or more monitored engine sensor values are used in a regression equation to normalize current engine regulator usage to a reference condition. The method further includes comparing the normalized current engine regulator usage to a historical value of engine regulator usage derived from prior takeoff data, and utilizing results of the comparison to indicate whether to perform proactive maintenance on the engine.