IP Library Granted Patent US 12,106,235
Granted Patent B2
US 12,106,235 · App. 16/360,480 · Granted Oct 1, 2024

System for forecasting aircraft engine deterioration using recurrent neural networks

Inventors: Sharath B. Nagaraja (Manchester, CT); Mathew R. Greco (Tolland, CT)
Assignee: RTX Corporation
G06Q10/04B64F5/60G05B23/0281G05B23/0283G06F18/25G06N3/04G08G5/0026G08G5/0039G08G5/006H04W4/029
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Quick Facts
Patent No.
US 12,106,235
App. No.
16/360,480
Granted
Oct 1, 2024
Kind
B2
Abstract

A method for forecasting aircraft engine deterioration includes creating a first fused data set corresponding to a first actual aircraft engine. The first fused data set includes at least one as manufactured parameter of the actual aircraft engine, expected operating parameters of the first actual aircraft engine, and actual operating parameters of the actual aircraft engine. The actual operating parameters of the actual aircraft engine include internal aircraft sensor data, and external flight tracking data. The method further includes predicting an expected engine deterioration of the first actual engine based on the expected operating parameters and the actual operating parameters of the first actual aircraft engine by applying the first fused data set to a forecasting model. The forecasting model is a recurrent neural network based algorithm, and the recurrent neural network based algorithm is trained via a plurality of second fused data sets corresponding to actual aircraft engines.

Claims (13)

1. A method for forecasting aircraft engine deterioration comprising:

flying an aircraft using a first actual aircraft engine and creating a first fused data set corresponding to the first actual aircraft engine, the first fused data set including at least one as manufactured parameter of the first actual aircraft engine, expected operating parameters of the first actual aircraft engine, and actual operating parameters of the first actual aircraft engine, the actual operating parameters of the first actual aircraft engine including internal aircraft sensor data, and external flight tracking data; and

predicting an expected engine deterioration of the first actual aircraft engine based on the expected operating parameters and the actual operating parameters of the first actual aircraft engine by applying the first fused data set to a forecasting model, wherein the forecasting model is a recurrent neural network based algorithm, and the recurrent neural network based algorithm is trained via a plurality of second fused data sets corresponding to actual aircraft engines;

including the step of taking off and landing the aircraft and the external flight tracking data includes at least one environmental condition at at least one of the takeoff and the landing; and

the aircraft flying an aircraft route and in an aircraft operating region, and the first actual aircraft engine is moved to at least one of a second aircraft route and a second aircraft operating region based upon the engine deterioration of the first actual engine.

2. The method of claim 1 , wherein a maintenance schedule for the first actual engine is changed based upon the prediction of expected engine deterioration, and maintenance is performed on the first actual aircraft engine based upon the changed maintenance schedule.

3. The method of claim 2 , wherein the maintenance includes a water wash of at least a portion of the first actual aircraft engine.

4. A method for forecasting aircraft engine deterioration comprising:

flying an aircraft using a first actual aircraft engine and creating a first fused data set corresponding to the first actual aircraft engine, the first fused data set including at least one as manufactured parameter of the first actual aircraft engine, expected operating parameters of the first actual aircraft engine, and actual operating parameters of the first actual aircraft engine, the actual operating parameters of the first actual aircraft engine including internal aircraft sensor data, and external flight tracking data; and

predicting an expected engine deterioration of the first actual aircraft engine based on the expected operating parameters and the actual operating parameters of the first actual aircraft engine by applying the first fused data set to a forecasting model, wherein the forecasting model is a recurrent neural network based algorithm, and the recurrent neural network based algorithm is trained via a plurality of second fused data sets corresponding to actual aircraft engines;

the aircraft flying an aircraft route and in an aircraft operating region, and the first actual aircraft engine is moved to at least one of a second aircraft route and a second aircraft operating region based upon the engine deterioration of the first actual engine; and

wherein a maintenance schedule for the first actual engine is changed based upon the prediction of expected engine deterioration, and maintenance is performed on the first actual aircraft engine based upon the changed maintenance schedule.

5. The method of claim 4 , wherein the maintenance includes a water wash of at least a portion of the first actual aircraft engine.

Assignments (4)
CHANGE OF NAME Recorded Jul 27, 2023
From: RAYTHEON TECHNOLOGIES CORPORATION
To: RTX CORPORATION
Reel/Frame 064402/0837 →
CORRECTIVE ASSIGNMENT TO CORRECT THE SPELLING ON THE ADDRESS 10 FARM SPRINGD ROAD FARMINGTONCONNECTICUT 06032 PREVIOUSLY RECORDED ON REEL 057190 FRAME 0719. ASSIGNOR(S) HEREBY CONFIRMS THE CORRECT SPELLING OF THE ADDRESS 10 FARM SPRINGS ROAD FARMINGTON CONNECTICUT 06032. Recorded Aug 19, 2021
From: UNITED TECHNOLOGIES CORPORATION
To: RAYTHEON TECHNOLOGIES CORPORATION
Reel/Frame 057226/0390 →
CHANGE OF NAME Recorded Aug 16, 2021
From: UNITED TECHNOLOGIES CORPORATION
To: RAYTHEON TECHNOLOGIES CORPORATION
Reel/Frame 057190/0719 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 21, 2019
From: NAGARAJA, SHARATH B.; GRECO, MATTHEW R.
To: UNITED TECHNOLOGIES CORPORATION
Reel/Frame 048661/0509 →