United Technologies Corporation

Steven Butler

Denman H James

Sean P McCutchan

Reade W James

William Donat

Sampath K Yerramalla

A method of real-time oil consumption is disclosed. A method of real-time oil consumption detection may include capturing a raw oil quantity, calculating a corrected oil quantity, calculating a predicted oil quantity, calculating a prediction error, and calculating an estimated oil consumption rate. Raw oil quantity may be captured from an oil quantity sensor in an engine. Corrected oil quantity may be calculated by taking raw oil quantity and applying environmental and engine operational conditions. Prediction error may be calculated by finding the difference between corrected oil quantity and predicted oil quantity. Oil consumption rate may be calculated by applying a regression algorithm to prediction error.

Method of real-time oil consumption detection

A method and system is developed that provides a confidence measure of a prediction of a fault in a gas turbine engine. The confidence measure is developed based upon evaluating the results of a plurality of past predictions and comparing them to an actual fault.

Method for calculating confidence on prediction in fault diagnosis systems

A method for modeling the performance of a gas turbine engine is provided. The method includes the steps of: 1) providing a processor; 2) inputting flight condition parameter data and engine output parameter data into a gas turbine engine model operating on the processor, which model includes a physics-based engine model that uses the flight condition parameter data to produce estimated engine output parameter data, and determines residuals from the engine output parameter data and the estimated engine output parameter data; 3) partitioning the flight condition parameter data and residuals into training data and testing data; 4) performing a correlation reduction on the training data, which analysis produces correlation adjusted training data; 5) performing an orientation reduction on the correlation adjusted training data, which reduction produces orientation adjusted training data; 6) reviewing the orientation adjusted training data relative to at least one predetermined criteria, and iteratively repeating the steps of performing a correlation reduction and an orientation reduction using the orientation adjusted training data if the criteria is not satisfied, and if the criteria is satisfied outputting the orientation adjusted training data; 7) producing estimated corrections to the orientation adjusted training data using one or more neural networks; 8) evaluating the neural adjusted data using the partitioned testing data; and 9) modeling the performance of the gas turbine using the estimated corrections to the orientation adjusted training data.

Method and system for modeling the performance of a gas turbine engine

A fault isolation method and system includes tailored fault isolators for each grouping of similar data to differentiate between one or several potential faults within any group.

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Columbia, CT, United States of America

Steven Butler