Aircraft Data Fusion for Automated Fault Correlation
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Solution Overview
Problem
Existing systems for aircraft data analysis rely heavily on human interpretation, which is cumbersome and time-consuming, and often yield limited accuracy in determining aircraft faults and maintenance needs, as they struggle to effectively fuse and analyze vast quantities of data from multiple sources.
Innovation Solution
A computer-implemented method and system that access and fuse data from various aircraft-related sources, detect anomalies, identify alert types and sources, and determine aircraft faults through correlation, using preconfigured processing rules and machine learning algorithms to provide automated fault prediction, diagnosis, and maintenance recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human interpretation is used to analyze aircraft operational data, then flexibility and adaptability are maintained, but the process becomes cumbersome, tedious, and time-consuming
Solution Approach 1:
The patent replaces the mechanical human interpretation process with an automated computer-based system that uses algorithms and machine learning models to analyze aircraft operational data, thereby eliminating the time-consuming and tedious nature of manual analysis while maintaining analytical capability
Solution Approach 2:
The system enables self-service automated analysis where the computer-based platform independently processes aircraft operational data, detects anomalies, and generates insights without requiring human intervention for each analysis task, thus resolving the contradiction between ease of operation and time consumption
2Measurement precision
If enterprise-level analytic systems consume multiple data streams to provide a composite view, then situational awareness accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex data analysis process into distinct modular components including data ingestion modules, anomaly detection modules, machine learning model modules, and reporting modules. Each module handles specific aspects of multi-source data processing independently, reducing overall system complexity while maintaining the ability to consume multiple data streams for improved fault detection accuracy
Solution Approach 2:
The system introduces intermediary layers including data normalization modules and feature extraction modules that mediate between raw multi-source data streams and the core analysis engine. These intermediaries standardize and preprocess data from multiple sources before analysis, enabling accurate composite view generation without directly increasing the complexity of the core system
3Productivity
If automated systems are implemented for data analysis, then productivity and speed are improved, but the requirement for sophisticated algorithms and processing power increases complexity
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models offline and pre-processing aircraft operational data to extract relevant features before analysis. This preparation work is performed in advance, allowing the automated system to process new data quickly with simpler real-time algorithms, thus achieving high productivity without requiring overly complex real-time processing
Solution Approach 2:
The system applies partial action by focusing automated analysis on specific critical parameters and anomaly patterns rather than attempting to analyze all aspects of aircraft operational data with equal depth. This selective approach achieves high productivity for critical fault detection while keeping the algorithmic complexity manageable by concentrating computational resources on the most important analysis tasks
Data Source
AI summary
Systems and methods for fusing and analyzing multiple sources of data from a plurality of aircraft-related data sources for fault determination and other integrated vehicle health management (IVHM) diagnostics are provided. More particularly, one or more portions of data from a plurality of aircraft-related data sources for one or more aircraft are accessed. One or more alerts indicative of a data anomaly within the one or more portions of aircraft-related data sources can be detected, wherein a type of alert and originating data source associated with each alert as well as optional related confidence scores can be identified. One or more aircraft faults can be determined based at least in part on a correlation of identified types and data sources for detected alerts. An output indicative of the determined one or more aircraft faults can be provided.