Component Removal Labeling for Predictive Vehicle Maintenance
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Solution Overview
Problem
In the aerospace industry, determining which vehicle components to remove or replace and when is complex due to their interactions with other components and systems, making it difficult to predict the need for maintenance effectively.
Innovation Solution
A computing device is configured to predict when a component should be removed from a vehicle by generating a component removal prediction model based on maintenance and removal records, and it determines the component's location on the vehicle using regular expression patterns applied to various data fields, allowing for the generation of a visual representation for user guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If components are inspected and tracked manually to determine removal needs, then maintenance can be performed, but the process is time-consuming and error-prone due to complex component interactions
Solution Approach 1:
The patent replaces manual mechanical inspection processes with an automated computer-based system that uses machine learning models and natural language processing to analyze maintenance records, removal records, and component interactions, thereby reducing inspection time while maintaining or improving accuracy
Solution Approach 2:
The system creates digital copies of maintenance records, removal records, and component data structures, processing these replicated information sets through automated algorithms to determine component removal needs without physically handling or manually reviewing each component record
2Measurement precision
If all vehicle components are monitored to predict removal needs, then maintenance precision improves, but the complexity of the system increases due to multiple data sources and interactions
Solution Approach 1:
The patent segments the complex maintenance prediction problem into distinct processing stages: obtaining maintenance records, obtaining removal records, processing component interactions, generating visual representations, and providing recommendations. Each stage handles specific data types and operations, reducing overall system complexity while maintaining comprehensive analysis
Solution Approach 2:
The system introduces intermediary data structures including component data structures that model component interactions, visual representation data structures that organize maintenance information, and recommendation data structures that synthesize analysis results. These intermediaries simplify the processing of complex multi-source data
3Measurement precision
If component location information is extracted from multiple data fields using pattern matching, then location accuracy improves, but the processing time increases due to applying regular expressions to multiple fields
Solution Approach 1:
The system performs preliminary processing by obtaining and structuring maintenance records and removal records before location extraction. It pre-identifies relevant data fields and prepares component data structures, so that when regular expression pattern matching is applied to extract locations, the process is more efficient and accurate
Data Source
AI summary
A computing device predicts whether a given component currently installed on a vehicle should be removed from the vehicle, and if so, identifies the location of the given component on the vehicle. The prediction is based on a prediction model that is generated by the computing device using maintenance data associated with the maintenance of components currently installed on, and/or previously removed from, the vehicle, as well as removal data associated with the previous removal of the same type of components from the vehicle. Information associated with the prediction is then output graphically to a display so that an operator can effect the removal of the component.