Aircraft Sensor Grouping for Targeted Maintenance Decisions
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Solution Overview
Problem
Current aircraft maintenance methods require extensive time and effort to determine which specific portions of an aircraft need maintenance, as they assess overall performance without identifying distinct components like wings, ailerons, or fuselage, leading to inefficiencies and increased costs.
Innovation Solution
A method and system that select sensor groupings corresponding to operationally distinct aircraft portions, using machine learning models trained on operational metrics derived from sensor data to accurately determine maintenance needs, reducing data requirements and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human inspectors perform physical checks or review sensor data to determine aircraft maintenance needs, then maintenance decisions can be made, but extensive time and effort are required
Solution Approach 1:
The patent replaces manual inspection methods with an automated machine learning system that processes sensor data. The system uses trained models to analyze operational data and automatically determine maintenance needs, substituting human inspectors with computational algorithms that can process data faster and more consistently.
Solution Approach 2:
The patent creates a virtual model of aircraft condition by processing and analyzing sensor data copies. The machine learning system works with replicated data sets rather than physical inspection, allowing multiple analyses to be performed simultaneously without additional physical effort.
2Reliability
If sensor data is used to assess overall aircraft performance, then maintenance needs can be identified, but specific portions of the aircraft requiring maintenance cannot be determined
Solution Approach 1:
The patent segments the aircraft into distinct operational portions (wings, fuselage, engines, etc.) and associates specific sensor groupings with each portion. The machine learning system analyzes data from these segmented sensor groups to identify which specific aircraft portion requires maintenance, rather than treating the aircraft as a single unit.
Solution Approach 2:
The patent applies different sensor groupings and analysis methods to different portions of the aircraft based on their specific operational characteristics. Each aircraft portion has tailored sensors and metrics that capture its unique condition, allowing the system to provide location-specific maintenance information.
3Measurement precision
If comprehensive sensor data is collected for all aircraft portions, then accurate maintenance determination can be made, but data requirements and processing complexity increase
Solution Approach 1:
The patent divides the comprehensive sensor data into segmented groups, with each group corresponding to a specific aircraft portion. This segmentation allows the system to process and analyze data in manageable units rather than handling all sensor data as a single complex data set, reducing processing complexity while maintaining accuracy.
Solution Approach 2:
The patent uses only the specific sensor groupings necessary for each aircraft portion rather than all available sensors. This partial action approach collects sufficient data for accurate maintenance determination without the complexity of processing excessive data from all aircraft systems.
Data Source
AI summary
In an example, a method for determining whether to perform aircraft maintenance is described. The method comprises selecting groupings of sensors and/or parameters associated with an aircraft type. The method comprises receiving feature data that corresponds to each grouping of sensors and/or parameters. The method comprises determining, from the feature data, values for predetermined operational metrics. The method comprises comparing the values to values for predetermined operational metrics that correspond to at least one other flight of the aircraft. The method comprises determining, based on comparing the values for the predetermined operational metrics, values of additional operational metrics. The method comprises training a machine learning model using at least the values for the additional operational metrics. The method comprises providing, based on an output of the machine learning model, an instruction to perform maintenance on one or more of the plurality of portions of the aircraft or another aircraft.