Aircraft Fault Prediction via Flight2Vec Embeddings
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Solution Overview
Problem
Current methods for identifying aircraft faults, particularly flight deck effects, are inefficient as they require building separate models for each fault and struggle to accurately determine correlations between maintenance messages and flight deck effects, leading to unscheduled interruptions and suboptimal maintenance.
Innovation Solution
The implementation of a machine learning-based system using a Flight2Vec model that generates embedding vectors for low and high priority messages, allowing for the prediction of target high priority messages based on co-occurrence and negative sampling, enabling the identification of correlations between faults within and across aircraft subsystems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate models are built for each fault to identify aircraft faults, then the system can provide detailed fault-specific analysis, but the device complexity and time consumption increase significantly
Solution Approach 1:
The patent combines multiple fault-specific models into a single unified machine learning model that processes all maintenance messages and flight deck effects simultaneously. This unified model uses embedding vectors to represent different fault types and maintains the ability to provide specific fault analysis while reducing overall system complexity and training time.
Solution Approach 2:
The unified machine learning model is designed to handle multiple fault types and message priorities within a single framework. The model uses a universal architecture that can identify correlations across different subsystems and fault categories, making it versatile for various aircraft conditions without requiring separate specialized models.
2Measurement precision
If separate models are built for each fault, then detailed fault analysis is possible, but the training time and computational resources increase
Solution Approach 1:
The patent merges the training process of multiple fault-specific models into a single unified training process. The unified model learns from all maintenance messages and flight deck effects simultaneously, significantly reducing total training time while maintaining the ability to accurately identify specific fault correlations through its embedding vector representations.
3Reliability
If traditional methods are used to monitor aircraft health, then all sensors are monitored, but the ability to predict major faults from minor faults is insufficient
Solution Approach 1:
The unified machine learning model performs preliminary analysis of maintenance messages to identify patterns and correlations that precede major flight deck effects. By continuously learning from low and high priority messages, the model predicts potential major faults before they occur, enabling proactive maintenance actions.
Solution Approach 2:
The system implements feedback mechanisms where the model continuously learns from actual flight data, maintenance outcomes, and flight deck effects. This feedback loop refines the embedding vectors and improves prediction accuracy over time, allowing the system to better identify correlations between minor maintenance messages and major faults.
Data Source
AI summary
A method for identifying aircraft faults, comprising: receiving a dataset comprising a plurality of low priority messages and a plurality of high priority messages, each low priority message identifying a minor aircraft fault and each high priority message identifying a major aircraft fault; for each low priority message, generating an embedding vector which maps the low priority message in an embedding space; for each high priority message, generating an embedding vector which maps the high priority message in the embedding space; providing, to a machine learning unit, the embedding vector for each low priority message of the plurality of low priority messages and the embedding vector for each high priority message of the plurality of high priority messages; and obtaining, from the machine learning unit, a probability of a target high priority message occurring based on each low priority message of the plurality of low priority messages.