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Prediction methods and systems for structural repair during heavy maintenance of aircraft

A technology of aircraft and predictive models, applied in general control systems, test/monitoring control systems, control/regulation systems, etc., can solve problems such as unpredictable downtime of aircraft

Active Publication Date: 2018-01-05
THE BOEING CO
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

additionally, overhauls result in unpredictable downtime for individual aircraft due to the variable demand for repair or replacement of components and the time devoted to repair, reproduction, and / or replacement of affected components

Method used

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  • Prediction methods and systems for structural repair during heavy maintenance of aircraft
  • Prediction methods and systems for structural repair during heavy maintenance of aircraft
  • Prediction methods and systems for structural repair during heavy maintenance of aircraft

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Embodiment approach

[0158] Clause 1. A method for creating a predictive model for structural repair during an overhaul of a fleet of aircraft each including a structural component, the method comprising the steps of:

[0159]performing the following for a first aircraft of the fleet of aircraft, wherein the first aircraft undergoes replacement of the structural component of the first aircraft during an overhaul of the first aircraft:

[0160] calculating a preselected data set of fatigue-related parameters from data collected during a first set of flights of the first aircraft, wherein each flight of the first set of flights occurs during an overhaul of the first aircraft before, and wherein, during each flight of the first set of flights, the first aircraft experienced an overstress event;

[0161] displaying said preselected data set; and

[0162] receiving a selection of said fatigue-related parameters;

[0163] performing the following for a second aircraft of the fleet of aircraft, wherein...

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Abstract

Hybrid feature selection methods include methods of creating a predictive model for structural repair during heavy maintenance in a fleet of aircraft. Methods include qualifying a qualification dataset of fatigue-related parameters calculated from data collected during a first group of flights of a first aircraft that experience a replacement of a structural component during heavy maintenance. Methods include receiving a qualified selection of the fatigue-related parameters and verifying a verification dataset of the qualified selection of the fatigue-related parameters calculated from data collected during a second group of flights of a second aircraft that experienced heavy maintenance without replacement of the structural component. Methods include receiving a set of verified and qualified fatigue-related parameters and building a predictive model for structural repair during heavy maintenance with a training dataset of the verified and qualified fatigue-related parameters calculated from data collected during additional flights of the fleet.

Description

technical field [0001] The present disclosure relates to predictive methods and systems for structural repair during aircraft heavy maintenance. Background technique [0002] Aircraft include structural components, such as airframes, which are generally designed to last the useful life of the aircraft. However, some components may not respond as expected to the stresses of use and the environment, and some aircraft may be used beyond their original design life. In such cases, repair or replacement of a structural component not originally designed for repair or replacement may result in significant downtime of the individual aircraft while the affected structural component is repaired or reproduced for replacement. [0003] For example, the F / A-18 Hornet first entered operational service in 1983. Now more than 30 years later, most F / A-18 Hornets in service operate at or beyond their original design life (6000-8000 hours). Ongoing operations rely on a structured approach fo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG05B23/0283G06Q10/20G05B23/024G06F30/15G06N5/022
Inventor J·M·埃辛顿L·司徒朗松
Owner THE BOEING CO
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