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 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 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 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: The present invention provides methods and tools to aggregate information stemming from a plurality of different classification tools and supportive evidential information to arrive at a unified classification estimate. The information fusion system according to the present invention has a plurality of sensors associated with the system, where each sensor is related to at least one class of the system and is data-related to the at least one class. A plurality of classification tools are each designed to receive selected and pre-processed outputs from the sensors and to generate classification outputs representing a state of at least one class of the system. An information fusion tool is configured to receive the outputs of the classification tools as well as evidential information as inputs, and has an hierarchical architecture which manipulates the inputs to generate an output of aggregated fused information for a particular class of the system.

Abstract: A method and apparatus for detecting an impending failure of a process sensor is provided. The method includes determining a sensor output noise component, and comparing the output noise component to a historical sensor output noise signature based on at least one of the range of span of the sensor and the process operating conditions. The apparatus includes a computing device including a processor and a memory communicatively coupled to the processor wherein the processor is programmed to execute a software product code segment that includes a comparator, a data historian, an isolator module, and a limit module. The computing device is configured to detect impending failure of a sensor using an output signal noise component of the sensor and wherein the sensor senses a parameter of a process associated with the computing device.

Abstract: A model-based trending process for a gas turbine engine that generates, in real-time, engine trend parameters from engine sensor data and ambient flight condition data to assess engine condition is described. The engine includes a plurality of sensors that are responsive to engine operations. The trending process is implemented using a commercially available processor coupled to the engine to monitor the engine operations, and having the desired processing speed and capacity. Engine health parameters are estimated and adjusted in a model for component diagnostics and fault detection and isolation. The trend parameters generated are retained.

Abstract: A model-based trending process for a gas turbine engine that generates, in real-time, engine trend parameters from engine sensor data and ambient flight condition data to assess engine condition is described. The engine includes a plurality of sensors that are responsive to engine operations. The trending process is implemented using a commercially available processor coupled to the engine to monitor the engine operations, and having the desired processing speed and capacity. Engine health parameters are estimated and adjusted in a model for component diagnostics and fault detection and isolation. The trend parameters generated are retained.

Abstract: A model-based trending process for a gas turbine engine that generates, in real-time, engine trend parameters from engine sensor data and ambient flight condition data to assess engine condition is described. The engine includes a plurality of sensors that are responsive to engine operations. The trending process is implemented using a commercially available processor coupled to the engine to monitor the engine operations, and having the desired processing speed and capacity. Engine health parameters are estimated and adjusted in a model for component diagnostics and fault detection and isolation. The trend parameters generated are retained.