Aircraft Hardware Identification Using Weighted Consensus Training
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Solution Overview
Problem
Aircraft hardware component suppliers and entities face inefficiencies in deriving consensus values due to unnecessary bandwidth usage, data entry errors, and lengthy processes, lacking access to consensus values and relying on inaccurate historical data.
Innovation Solution
A machine-learned model is trained to determine consensus values for aircraft hardware components, utilizing a weighting model to filter and update data, generating a searchable database with confidence outputs for accurate identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing processes are used to derive hardware component values through network communications, then consensus values can be obtained, but bandwidth is wasted and time is excessive
Solution Approach 1:
The system pre-processes and stores hardware component data from multiple sources in a database before queries are made. This preliminary action includes collecting, validating, and organizing data so that when a query is received, the system can quickly retrieve and process pre-prepared information rather than performing lengthy network communications and data gathering in real-time.
Solution Approach 2:
The system creates a local copy of hardware component data from multiple sources and stores it in a database. Instead of repeatedly querying multiple entities through network communications, the system maintains local copies of the data that can be quickly accessed and processed, significantly reducing network bandwidth usage and response time while maintaining data accuracy.
2Productivity
If existing processes are used to derive hardware component values, then consensus values can be obtained, but data entry errors occur reducing accuracy
Solution Approach 1:
The system automatically retrieves, validates, and processes hardware component data from multiple sources without manual data entry. The automated process includes querying databases, validating data formats, resolving conflicts through predefined rules, and storing results, eliminating human errors associated with manual data entry while maintaining high productivity in deriving consensus values.
3Measurement precision
If large data lakes of historical training data are maintained for model training, then model accuracy can be improved, but system complexity and resource requirements increase
Solution Approach 1:
The system extracts only the essential features and data points needed for training the machine learning model from the available hardware component data, rather than maintaining and processing entire large-scale data lakes. This extraction approach focuses on relevant attributes such as component specifications, manufacturer data, and validated measurements, reducing infrastructure complexity while maintaining model training effectiveness.
Data Source
AI summary
A system and a method are disclosed for training a machine-learned model. A device retrieves entries from a database that each correspond a hardware component to a value. The device inputs the entries into a weighting model, and the weighting model outputs weights for the values. The device generates a training set including data formed by pairing each respective hardware component to its respective weighted value, and trains the machine-learned model using the training set. The device receives new data comprising a hardware component and a respective value, determines weights therefor, and re-trains the machine-learned model accordingly. Responsive to detecting a trigger, the device uses the machine-learned model to generate a searchable database, and outputs results to search queries including a value for a queried hardware component and a confidence that the value is correct.